Topics

• Deep Learning Wizard - to-end deep learning pipeline. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• The Fundamental Concepts of PyTorch - contained examples. |
• The machine learning glossary
• Reinforcement Learning 101
• Machine learning operations (MLops) - Machine Learning Operations |
• Deploy for targeted Use Cases (Gartner)
• Introduction into AI and ML
• Machine learning algorithms
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Convolution neural network
• AI Track Notes
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Artificial Intelligence Expert in 2020
• Generative Adversarial Network (GANs)
• Python - Data Science
• Data Science
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Webanno - annotation tool |
• Machine Learning
• Machine Learning for Cyber Security
• MeshTensorFlow
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Kneed, kneedle algorithm, Python
• Qlib, Quant investment strategies, investement platform - oriented quantitative investment platform. It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. |
• Artificial Intelligence
• Data Visualization
• Software Architecture
• Interview Prepartion Data Science
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science, Machine Learning Projects
• Machine learning
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Advances in AI
• Research Papers on AI
• Programming
• Deep Learning (an MIT Press Book)
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Machine Learning
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Machine Learning
• Machine Learning
• ML Modelling
• Math
• Python - Nearest Neighbors with Python |
• Data Science
• Data Science
• Data Science
• Adopting the power of conversational UX
• Data Engineering
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Mining
• Data Science
• Computer Vision
• Neural Networks
• Several Tutorials Notebooks for Python
• Data Science
• R Programming
• Git
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Convolution Neural Networks
• AI Podcast - Jeremy Howard | AI Podcast Clips |
• Machine Learning, Deep Learning, Python, R, free course
• AI Expert Roadmap
• Projects that matter Work that matters Data Science for Social Good. - defined problems, and to help solve those problems. |
• Mathematics for Machine Learning.
• Grokking Deep Learning.
• Pandas Cheat Sheet.
• NumPy and Pandas Tutorial – Data Analysis with Python
• Introduction to Python Programming.
• Deep learning chapter 1: what is a Neural Network?
• A Short Practical Introduction to Machine Learning: Predicting Survival on the Titanic! - along with me if you’d like. |
• Elements of AI free online course!
• Artificial Intelligence
• Machine Learning Engineering book written by Andriy Burkov.
• A visual introduction to machine learning
• Face it. Your Project Requirements are Poorly Written! podcast
• "Machine Learning" youtube playlist by StatQuest with Josh Starmer
• "CS50's Introduction to Artificial Intelligence with Python 2020" by CS50
• ML Cheatsheet
• MIT 6.S191 (2018): Deep Learning playlist by Alexander Amini
• Full Stack Deep Learning
• Deep learning (Convolution neural network) with google street view
• Deep learning, machine learning, python, algorithms and pytorch - world projects. Earn certifications. Interact with a global community. |
• Python
• Artificial Intelligence for Business Leaders
• Deep learning, and transfer learning
• Deep learning, and machine learning topics
• Artificial Intelligence, free content, Stanford
• Data science - oriented courses. |
• SCUM
• Neural Networks And Deeplearning
• Numpy Tutorial
• NumPy for Matlab users
• Artificial Neural Networks (ANN), Keras, Python, R
• Neural Networks - Université de Sherbrooke |
• Machine learning, pytorch, python, data analysis
• Awesome Artificial Intelligence (AI)
• Machine Learning, Roadmap 2020
• Reinforcement Learning 101
• Artificial neural networks
• Machine learning
• Machine Learrning - world examples. Yes, again. |
• Product Managers, AI, Machine Learning
• Reinforcement Learning
• Accuracy, Precision, Recall, and F1 Score
• Secure and Private AI by Udacity
• Statistics, Classification, Prediction
• Statistics, Classification Accuracy
• Machine learning - nearest- neighbor and python. |
• Data Science - data sciencecist |
• Machine learning
• Machine Learning
• Machine learning, Deep learning - engineering and engineering students. |
• Machine learning
• Machine Learning
• Machine learning
• Data science - 370+ Free Tutorials. |
• Deploy for targeted Use Cases (Gartner)
• Machine learning, data science, Mathematics
• Deploy for Targeted Use Cases (gartner)
• Business Problem before Data (PWC)
• Business Problem Before Data (PWC)
• Success depends on Data (IBM THINK)
• Success depends on Data (PWC)
• Amazon web services (AWS). - on. |
• Python
• Data Science
• AI - data and free platform built by the research community to facilitate the collaborative development of AI |
• (Udacity) Lesson 2.3 Using AI and ML in Business
• Machine Learning Mastery
• Graphic design platform
• Python, Java, Kotlin - on projects with step-by-step implementations, various examples and exercises. I found this to be very valuable to me and i hope that other students will find it the same way :) Happy coding everyone!!! + Accessing it by the link below will give students a 3 months free trial : ) |
• Machine learning, deep learning, mathematics
• Data analysis
• Statistics
• Machine learning, AI - on projects. |
• Machine learning, deep learning, artificial neural networks
• Python
• Product manager
• Product Design
• Python
• AI Books
• Statistics; Math
• Text Classification using Python and different libraries and AI Business Solutions articles
• Deep Learning Cheat Sheet
• (Udacity) Lesson 3.1 Data Fit And Annotation
• Machine Learning
• A/B Testing, free course, udacity
• Machine Learning, unsupervised Learning - course Machine Learning Series and is offered at Georgia Tech as CS7641. |
• machine learning, deep learning, NLP
• Machine learning, AI, deep learning
• AI Engineer
• AI, Natural Language Processing with Neural Networks
• Machine learning, python
• Python
• Python
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Data Science, Machine Learning
• Machine learning, R
• Machine Learning
• Python
• Tensorflow
• Data Science
• Run Jupyter Notebook in the cloud
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Rasa Chatbot
• Lessons and notebooks on machine learning and applied machine learning in production.
• Machine Learning Zero to Hero (Google I/O'19)
• Deep Learning A-Z
• Script of lessons
• Script of Lesson 3
• A Course on ML Production Systems
• Ethics of AI - online course
• AI in healthcare
• Become a Data Scientist
• Big Data - infinite data. |
• AI Podcast
• AI Podcast
• Python NLTK (2009)
• Elements of AI (course for free)
• Linear Algebra
• Statistics
• Machine Learning Podcast
• Allen Institute for AI Podcast
• Machine Learning
• Python
• Machine Learning and NLP
• Deep Learning with PyTorch
• Coursera Mathematics for Machine Learning Specialization
• First Steps in Linear Algebra for Machine Learning
• Coursera Machine Learning for All
• The impact of ai on project management
• Ai in project management
• Visualization of Data Structures and Algorithms
• Introduction to the TensorFlow Challenges
• Data Science Math Skills - calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. |
• Innovations in Investment Technology: Artificial Intelligence - driven online wealth management platforms, robo-advisors, and learn how they work and why they’re successful. |
• 180 Data Science and Machine Learning Projects with Python
• Artificial Intelligence (AI) - world problems including, search, games, machine learning, logic, and constraint satisfaction problems. |
• AI - get started - ai. What should I read? What should I watch? What should I do? |
• Learning AI - intelligence |
• GANs: Generative Adversarial Networks
• Statistics
• Deep Learning
• Pytorch
• The AI Podcast
• Confusion Matrix
• Data Science
• Artificial Neural Networks In Linguistic Data Processing
• Python
• Annotated Data for Machine Learning
• AI model vs carbon emission
• AI project ideas topics for beginners
• Free must read books-machine learning
• Machine Learning Project Ideas
• Linear algebra - depth course with 34 lectures by MIT will help you get started with it Down pointing backhand index |
• Recurrent Neural Networks
• Entire Computer Science Curriculum
• Computer Vision - Computer Vision Lecture Notes 1 |
• Computer Vision - Computer Vision Lecture Notes 2 |
• Linear Algebra
• Data Science Book Advise
• Data Analytics
• Data Science - Recommended curriculum for intro-level data science self-study |
• Algorithms
• C++
• SQL - Recommend for at least intermediate level. |
• Artificial Intelligence
• Python
• Python
• R
• AI - Unsupervised Learning
• AI NLP language understanding - up sentences show that AIs still don’t really understand language |
• AI language model
• Data Visualization - reference guide. |
• AI-Bias
• AI - biases- how to overcome them - biases- how to overcome them |
• Data Engineering
• Data Science - ish” criteria |
• Data Analysis - 19 Data Analysis with Python |
• Superintelligence (AI/Ethics) - ber of scientists, philosophers and technologists have revived the discussion about the potentiallycatastrophic risks entailed by such an entity. In this article, we trace the origins and developmentof the neo-fear of superintelligence, and some of the major proposals for its containment. We arguethat total containment is, in principle, impossible, due to fundamental limits inherent to comput-ing itself. Assuming that a superintelligence will contain a program that includes all the programsthat can be executed by a universal Turing machine on input potentially as complex as the state ofthe world, strict containment requires simulations of such a program, something theoretically (andpractically) impossible. |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Natural language predicts viral escape, NLP - 1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. |
• NLP (Introduction)
• Bayesian Reasoning and Machine Learning
• Grasshoper. Coding App for beginners
• Ken Jee's Data Science YouTube Channel
• Introduction to Deep Learning from Logical Calculus to Artifical Intelligence
• AI - robot-empathy-deception
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science
• Guide to AI in retail
• AI-retail - categories |
• Software Testing
• Amazon Web Services (AWS)
• Women Who Code Initiative - the largest and most active community of engineers dedicated to inspiring women to excel in technology careers. |
• Apple watch can detect covid-19 - 19 before symptoms arise new study shows |
• Data Science Portfolio
• Inception - annotation tool |
• CATMA - annotation tool |
• Product Management for AI by Google PM - life examples |
• Data Science Portfolio
• Python
• Statistics-Probability
• Python - A self study course |
• Machine Learning
• OpenCV
• Design Patterns
• Data Structures
• Python
• Python
• AI and digital marketing
• Unity ML
• The Cambridge handbook of artificial intelligence
• Artificial Intelligence: A Modern Approach
• Artificial Intelligence: A System Approach
• Introduction to Machine Learning
• Machine learning for enterprises: Applications, algorithm selection, and challenges
• Measurementality (Podcast)
• AI and Well-Being - being |
• Tech ethics
• List of Math Formulas
• The People + AI Guidebook
• Machine Learning
• Federated Learning - preserving technologies, explains in 4 minutes federated learning and how it helps all machine learning practitioners to protect their users’ privacy by default. |
• Deep learning
• Deeplearning
• Seven legal questions for data scientists
• Cloud Data Warehouse Performance Testing
• Python
• Python
• Linkedln
• Statistics
• Web Scraping, Collecting Data Tools
• Backpropagation, stochastic gradient descen - propagation algorithms clear in training between networks. |
• Data Science
• Statistics
• Data Science
• Statistics
• Statistics
• Data Science
• Machine Learning
• Programming
• Deep learning
• Introduction to Artificial Intelligence - solving, reasoning with uncertainty, machine learning, and data mining, neural networks, and reinforcement learning. |
• Introduction to AI with Python - playing engines, handwriting recognition, and machine translation. |
• The hard lessons of modeling the coronavirus pandemic - 19, getting a clear view of the virus has been difficult. In the fight against COVID-19, disease modelers have struggled with misunderstanding and misuse of their work. They have also come to realize how unready the state of modeling was for this pandemic. |
• Trove - crowdsourcing marketplace where developers can gather images for AI models from regular individuals
• Automatic scripts exports-nbautoexport-jupyter-code-review
• TensorFlow Federated - source framework for machine learning and other computations on decentralized data. |
• Federated Learning Comics
• Computer Vision
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Tensorflow
• Isotonic Regression and Pava Algorithm
• Data Science
• Machine Learning
• Software Testing Life Cycle
• Machine Learning
• Agile Development
• Machine Learning
• AI reading list: 8 interesting books about artificial intelligence to check out
• Comet is doing for ML what Github did for code
• data science
• Artificial Intelligence
• Data Visualization
• Tensorflow serving model
• How to think like a programmer — lessons in problem solving
• LinkedIn Skills: How to Add the Right Skills to LinkedIn
• Python, drone programming
• What to expect in your first data science role
• Machine Learning
• NetAdapt - Aware Neural Network Adaptation for Mobile Applications |
• Data Visualization
• Software Architecture
• Leetcode, prepare for technical interviews - organized tool that helps you get the most out of LeetCode by providing structure to guide your progress towards the next step in your programming career |
• Three things to do when you don't have a computer science degree
• Deep Learning
• Algorithms
• Python - TensorFlow with Python! Solve problems with cutting edge techniques! |
• Data Science
• A Single Equation that Rules the World
• Statistics the complete mini course
• Analytics: The complete minicourse
• Machine Learning - Google |
• Computer Vision - CNN & S-CNN — Multi-Region & Semantic-aware CNNs |
• Java
• Python - of-Sample Forecasts with ARIMA in Python |
• Java
• C
• Data Science
• Data Science - Learn Data Science in 10 Hours |
• Data Analytics
• concepts and philosophy of AI by Patrick Hebron
• AI Crash Course
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Combining artificial intelligence and augmented reality in mobile apps
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• SEER: The start of a more powerful, flexible, and accessible era for computer vision - supERvised), a new billion-parameter self-supervised computer vision model that can learn from any random group of images on the internet. |
• Introduction to Threshold-Moving for Imbalanced Classification
• Calculate Precision, Recall, and F-Measure for Imbalanced Classification
• 4 Types of Classification Tasks in Machine Learning
• User Persona Examples
• Growth Strategy
• Market Strategy
• TARGET MARKET
• Style classification and prediction of residential buildings
• Machine Learning, Architectural Styles and Property Values - scale comparison of predictions and expert classifications. |
• PII
• Personally Identifiable Information (PII)
• Rethinking Design Tools in the Age of Machine Learning
• World Speeches - Youtube channel from one of our study group #sg_ai_world - Youtube channel from one of our study group #sg_ai_world |
• A Unified Tool for the Education of Humans and Machines
• Computer Vision & AI
• Machine learning
• SQL
• Artificial Intelligence
• Python - through with Matplotlib for Data Science |
• Python
• MATLAB
• Medical AI TED Talk
• Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer - AI Track |
• Data Science
• Data Science
• AWS
• Data Mining
• Marketing, Digital transformation, Trends - era trends that will have a lasting impact on the products and experiences people want |
• Consumer Trends after Covid-19
• Bias in AI - known artificial intelligence experts, a long-simmering research controversy burst into the open. |
• Numeric Analysis - Seidel Method |
• Web Developping - HTML, CSS, JS, Node, and More! |
• Machine Learning
• Machine Learning
• Machine Learning
• Data Science
• AI for Frontend Developers
• Machine Learning
• Learn Google Drive From Beginner to Advanced
• Data Science
• Python
• Go
• Machine Learning
• Python
• Starting Out with Python - solving skills, without assuming any previous programming experience. With easy-to-understand examples, pseudocode, flowcharts, and other tools, the student learns how to design the logic of programs then implement those programs using Python. This book is ideal for an introductory programming course or a programming logic and design course using Python as the language. |
• Artificial Intelligence
• Artificial Intelligence
• Artificial Intelligence
• AI for Product Managers
• MySQL
• Machine Learning - By-Step |
• Don't build chatbot end to end
• Wireflow for chatbot
• Artificial Intelligence
• Python
• Python
• Deep Learning
• Python
• AI ethics
• Python
• Machine Learning
• AI.Applications
• SQL
• How to get a job in data science (with no work experience)
• Data Science
• Artificial Intelligence - -especially your non-technical colleagues--to take. |
• C#
• Data Engineering
• Machine Learning
• Deep Learning
• Data Science
• NLP
• Artificial Intelligence - based leading global community for AI education and AI career opportunities. |
• NLP - source NLP library for urdu language. It comes with a lot of battery included features to help you process Urdu data in the easiest way possible. |
• Artificial Intelligence - of-five-game competition, coined The DeepMind Challenge Match. |
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Companies hiring data scientists right now - website with a lot of job info |
• Python
• Data Science
• Deep Learning MIT 6S.191 couse "Intro to Deep Learning"
• imbalanced datasets in machine learning
• Model Evaluation
• precision and recall - class classification problems, we often think that the only way to evaluate performance is by computing the accuracy which is the proportion or percentage of correctly predicted labels over all predictions. |
• Overview of Modelling
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Python
• Data Science Portfolio
• Machine Learning
• Machine Learning
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Data Science
• Python
• Data Science
• Web Scraping, Collecting Data Tools
• Machine Learning - to-End, Transferable Deep RL for Graph Optimization |
• Machine Learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Artificial Intelligence
• Data Visualization
• imbalanced datasets in machine learning
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• A Unified Tool for the Education of Humans and Machines
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Audio Classification Model
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• DEEP LEARNING
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
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• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
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• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
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• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
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• Become a Data Scientist
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• Data Science Portfolio
• Machine Learning
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• A Unified Tool for the Education of Humans and Machines
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Data Science
• Data Science
• Web Scraping, Collecting Data Tools
• Data Visualization
• Machine Learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Python
• Artificial Intelligence
• Data Science
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Artificial Intelligence for Business Leaders
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Web Scraping, Collecting Data Tools
• imbalanced datasets in machine learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Become a Data Scientist
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Data Science
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Python
• Machine Learning
• Data Science
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Artificial Intelligence
• Data Visualization
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Data Science
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science Portfolio
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Machine Learning
• Interactive Plots only with Matplotlib
• Data Science - Recommended curriculum for intro-level data science self-study |
• Deep Learning with CIFAR-10 - 10 |
• Data Science - ish” criteria |
• Data Science
• Web Scraping, Collecting Data Tools
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Become a Data Scientist
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• Interactive Plots only with Matplotlib
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Data Visualization
• Machine Learning
• Artificial Intelligence
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• imbalanced datasets in machine learning
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• A Unified Tool for the Education of Humans and Machines
• Data Science - ish” criteria |
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Data Science
• Become a Data Scientist
• Data Science - Recommended curriculum for intro-level data science self-study |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Web Scraping, Collecting Data Tools
• Data Science Portfolio
• Machine Learning
• Machine Learning
• Artificial Intelligence
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Visualization
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• A Unified Tool for the Education of Humans and Machines
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Analysis with Python: Zero to Pandas - world projects. Earn certifications. Interact with a global community. |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Machine Learning
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Research Papers on AI
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science - Recommended curriculum for intro-level data science self-study |
• Artificial Intelligence for Business Leaders
• Data Science - ish” criteria |
• Reinforcement Learning 101
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• AI language model
• Data Science
• Data Science Portfolio
• Machine Learning
• Machine Learning
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Artificial Intelligence
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Visualization
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Web Scraping, Collecting Data Tools
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• imbalanced datasets in machine learning
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• A Unified Tool for the Education of Humans and Machines
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Become a Data Scientist
• Artificial Intelligence for Business Leaders
• Interactive Plots only with Matplotlib
• Reinforcement Learning 101
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Machine Learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science
• Python
• Data Science - ish” criteria |
• Machine Learning
• Data Science
• Python
• Data Science
• Web Scraping, Collecting Data Tools
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• A Unified Tool for the Education of Humans and Machines
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Become a Data Scientist
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Interactive Plots only with Matplotlib
• Artificial Intelligence for Business Leaders
• Deep Learning with CIFAR-10 - 10 |
• Reinforcement Learning 101
• Data Science
• Data Science Portfolio
• Machine Learning
• Python
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Machine Learning
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Data Science
• Data Science - Recommended curriculum for intro-level data science self-study |
• Python
• Data Science - ish” criteria |
• Data Science
• Web Scraping, Collecting Data Tools
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Resume Bullet Point Examples That Get Interviews
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Artificial Intelligence for Business Leaders
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• AI, Human Intelligence
• Data Science - Recommended curriculum for intro-level data science self-study |
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Become a Data Scientist
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Women who code (network)
• Data Science Portfolio
• Machine Learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Python
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• Data Science
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Reinforcement Learning 101
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Web Scraping, Collecting Data Tools
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Machine Learning
• Artificial Intelligence
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• A Unified Tool for the Education of Humans and Machines
• imbalanced datasets in machine learning
• Data Science
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Web Scraping, Collecting Data Tools
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Python
• Data Science
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Science fiction hasn’t prepared us to imagine machine learning - 3 (2020)—which can turn a prompt into an imaginary news story—to, most recently, CLIP and DALL-E (2021), which can translate verbal descriptions into images." |
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• imbalanced datasets in machine learning
• OpenAI - shot” capabilities of GPT-2 and GPT-3. |
• A Unified Tool for the Education of Humans and Machines
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Data Science
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Interactive Plots only with Matplotlib
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Deep Learning with CIFAR-10 - 10 |
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Web Scraping, Collecting Data Tools
• Data Science - Recommended curriculum for intro-level data science self-study |
• Data Science Portfolio
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Machine Learning
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science - ish” criteria |
• Data Science
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Data Science
• Python
• Data Science
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Python
• Machine Learning
• Data Science
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Data Science
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Artificial Intelligence for Business Leaders
• Reinforcement Learning 101
• Data Science - Recommended curriculum for intro-level data science self-study |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• DEEPLIZARD is Building Collective Intelligence by offering deep learning courses. - Python Deep Learning Neural Network API, Neural Network Programming - Deep Learning with PyTorch |
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Data Science Portfolio
• Machine Learning
• Python
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Web Scraping, Collecting Data Tools
• Data Science
• Data Science
• Python
• Machine Learning
• Data Science
• Data Visualization
• Machine Learning
• Artificial Intelligence
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Data Science
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• imbalanced datasets in machine learning
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines
• Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
• Python
• Data Science - Recommended curriculum for intro-level data science self-study |
• Artificial Intelligence for Business Leaders
• Machine Learning
• Reinforcement Learning 101
• Data Science - ish” criteria |
• AI Robotics + Theory of mind - in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
• Machine Learning, unsupervised Learning - course Machine Learning Series and is offered at Georgia Tech as CS7641. |
• Python, keras, pandas - to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
• Datasets for Machine Learning and Data Science - access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
• Become a Data Scientist
• Data Science
• Interactive Plots only with Matplotlib
• Deep Learning with CIFAR-10 - 10 |
• Python
• Data Science Portfolio
• Machine Learning
• Web Scraping, Collecting Data Tools
• Data Science
• Data Science
• Multi-Class Metrics - scores are used, and how to calculate them in a multi-class setting. |
• Data Science
• Data Science - have Chrome Extensions For Machine Learning Engineers And Data Scientists |
• Machine Learning
• Artificial Intelligence
• Data Visualization
• Multi-Class Metrics Made Simple, Part I: Precision and Recall - class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
• I Landed a Job at an AI Startup after Studying for Only 4 Months
• Multi-Class Metrics Made Simple, Part II: the F1-score - score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
• imbalanced datasets in machine learning
• Two Macro-F1's - F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
• A Unified Tool for the Education of Humans and Machines