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https://github.com/amitness/learning

A log of things I'm learning
https://github.com/amitness/learning
deep-learning generative-ai learning-resources llms machine-learning nlp python
Last synced: 2 days ago
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A log of things I'm learning
Host: GitHub
URL: https://github.com/amitness/learning
Owner: amitness
License: mit
Created: 2017-11-26T17:37:44.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2024-11-07T15:40:16.000Z (about 1 month ago)
Last Synced: 2024-11-26T03:03:59.205Z (16 days ago)
Topics: deep-learning, generative-ai, learning-resources, llms, machine-learning, nlp, python
Homepage: https://twitter.com/amitness
Size: 971 KB
Stars: 6,341
Watchers: 199
Forks: 846
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

my-awesome - amitness/learning - learning,generative-ai,learning-resources,llms,machine-learning,nlp,python pushed_at:2024-12 star:6.3k fork:0.8k A log of things I'm learning (Others)
README

        # learning

A running log of things I'm learning to build strong core software engineering skills while also expanding my knowledge of [adjacent technologies](http://www.effectiveengineer.com/blog/master-adjacent-disciplines) a little bit [everyday](https://jamesclear.com/continuous-improvement).

**Updated**: Once a month | **Current** **Focus**: Generative AI

## Core Skills

### Python Programming

|Resource|Progress|

|---|---|

|[Datacamp: Writing Efficient Python Code](https://www.datacamp.com/courses/writing-efficient-python-code)|✅|

|[Datacamp: Writing Functions in Python](https://www.datacamp.com/courses/writing-functions-in-python)|✅|

|[Datacamp: Object-Oriented Programming in Python](https://www.datacamp.com/courses/object-oriented-programming-in-python)|✅|

|[Datacamp: Intermediate Object-Oriented Programming in Python](https://www.datacamp.com/courses/intermediate-object-oriented-programming-in-python)|✅|

|[Datacamp: Importing Data in Python (Part 1)](https://www.datacamp.com/courses/importing-data-in-python-part-1)|✅|

|[Datacamp: Importing Data in Python (Part 2)](https://www.datacamp.com/courses/importing-data-in-python-part-2)|✅|

|[Datacamp: Intermediate Python for Data Science](https://www.datacamp.com/courses/intermediate-python-for-data-science)|✅|

|[Datacamp: Python Data Science Toolbox (Part 1)](https://www.datacamp.com/courses/python-data-science-toolbox-part-1)|✅|

|[Datacamp: Python Data Science Toolbox (Part 2)](https://www.datacamp.com/courses/python-data-science-toolbox-part-2)|✅|

|[Datacamp: Developing Python Packages](https://www.datacamp.com/courses/developing-python-packages)|✅|

|[Datacamp: Conda Essentials](https://www.datacamp.com/courses/conda-essentials)|✅|

|[Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit](https://www.youtube.com/watch?v=WkUBx3g2QfQ&feature=youtu.be)|✅|

|[Datacamp: Working with Dates and Times in Python](https://www.datacamp.com/courses/working-with-dates-and-times-in-python)|✅|

|[Datacamp: Command Line Automation in Python](https://www.datacamp.com/courses/command-line-automation-in-python)|⬜|

|[Datacamp: Unit Testing for Data Science in Python](https://www.datacamp.com/courses/unit-testing-for-data-science-in-python)|✅|

|[Book: Python 201](https://leanpub.com/python201)|⬜|

|[Book: Writing Idiomatic Python 3](https://www.amazon.com/Writing-Idiomatic-Python-Jeff-Knupp-ebook/dp/B00B5VXMRG)|⬜|

|[Book: Test Driven Development with Python](http://chimera.labs.oreilly.com/books/1234000000754/index.html)|⬜|

|[Article: Python's many command-line utilities](https://www.pythonmorsels.com/cli-tools/)|⬜|

|[Article: A Programmer’s Introduction to Unicode](https://www.reedbeta.com/blog/programmers-intro-to-unicode/)|⬜|

|[Article: Introduction to Memory Profiling in Python](https://www.datacamp.com/tutorial/memory-profiling-python)|✅|

|[Article: Profiling Python code with memory_profiler](https://www.wrighters.io/profiling-python-code-with-memory_profiler/)|✅|

|[Article: How to Use "memory_profiler" to Profile Memory Usage by Python Code?](https://coderzcolumn.com/tutorials/python/how-to-profile-memory-usage-in-python-using-memory-profiler)|✅|

### Data Structures and Algorithms

|Resource|Progress|

|---|---|

|[Book: Grokking Algorithms](https://www.manning.com/books/grokking-algorithms)|✅|

|[Book: The Tech Resume Inside Out](https://thetechresume.com)|✅|

|[Neetcode: Algorithms and Data Structures for Beginners](https://neetcode.io/courses/dsa-for-beginners/0)|✅|

|[Udacity: Intro to Data Structures and Algorithms](https://www.udacity.com/course/technical-interview--ud513)|✅|

### Linux & Command Line

|Resource|Progress|

|---|---|

|[Datacamp: Introduction to Shell for Data Science](https://www.datacamp.com/courses/introduction-to-shell-for-data-science)|✅|

|[Datacamp: Introduction to Bash Scripting](https://www.datacamp.com/courses/introduction-to-bash-scripting)|✅|

|[Datacamp: Data Processing in Shell](https://www.datacamp.com/courses/data-processing-in-shell)|✅|

|[MIT: The Missing Semester](https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J)|✅|

|[Udacity: Linux Command Line Basics](https://www.udacity.com/course/linux-command-line-basics--ud595)|✅|

|[Udacity: Shell Workshop](https://www.udacity.com/course/shell-workshop--ud206)|✅|

|[Udacity: Configuring Linux Web Servers](https://www.udacity.com/course/configuring-linux-web-servers--ud299)|✅|

### Version Control

|Resource|Progress|

|---|---|

|[Udacity: Version Control with Git](https://www.udacity.com/course/version-control-with-git--ud123)|✅|

|[Datacamp: Introduction to Git for Data Science](https://www.datacamp.com/courses/introduction-to-git-for-data-science)|✅|

|[Udacity: GitHub & Collaboration](https://www.udacity.com/course/github-collaboration--ud456)|✅|

|[Udacity: How to Use Git and GitHub](https://www.udacity.com/course/how-to-use-git-and-github--ud775)|✅|

### Databases

|Resource|Progress|

|---|---|

|[Udacity: Intro to relational database](https://www.udacity.com/course/intro-to-relational-databases--ud197)|✅|

|[Udacity: Database Systems Concepts & Design](https://www.udacity.com/course/database-systems-concepts-design--ud150)|⬜|

|[Datacamp: Database Design](https://www.datacamp.com/courses/database-design)|⬜|

|[Datacamp: Introduction to Databases in Python](https://www.datacamp.com/courses/introduction-to-relational-databases-in-python)|⬜|

|[Datacamp: Intro to SQL for Data Science](https://www.datacamp.com/courses/intro-to-sql-for-data-science)|✅|

|[Datacamp: Intermediate SQL](https://www.datacamp.com/courses/intermediate-sql)|⬜|

|[Datacamp: Joining Data in PostgreSQL](https://www.datacamp.com/courses/joining-data-in-postgresql)|⬜|

|[Udacity: SQL for Data Analysis](https://www.udacity.com/course/sql-for-data-analysis--ud198)|⬜|

|[Datacamp: Exploratory Data Analysis in SQL](https://www.datacamp.com/courses/sql-for-exploratory-data-analysis)|⬜|

|[Datacamp: Applying SQL to Real-World Problems](https://www.datacamp.com/courses/applying-sql-to-real-world-problems)|⬜|

|[Datacamp: Analyzing Business Data in SQL](https://www.datacamp.com/courses/analyzing-business-data-in-sql)|⬜|

|[Datacamp: Reporting in SQL](https://www.datacamp.com/courses/reporting-in-sql)|⬜|

|[Datacamp: Data-Driven Decision Making in SQL](https://www.datacamp.com/courses/data-driven-decision-making-with-sql)|⬜|

|[Datacamp: NoSQL Concepts](https://www.datacamp.com/courses/nosql-concepts)|⬜|

|[Datacamp: Introduction to MongoDB in Python](https://www.datacamp.com/courses/introduction-to-using-mongodb-for-data-science-with-python)|⬜|

### Backend Engineering

|Resource|Progress|

|---|---|

|[Udacity: Authentication & Authorization: OAuth](https://www.udacity.com/course/authentication-authorization-oauth--ud330)|⬜|

|[Udacity: HTTP & Web Servers](https://www.udacity.com/course/http-web-servers--ud303)|⬜|

|[Udacity: Client-Server Communication](https://www.udacity.com/course/client-server-communication--ud897)|⬜|

|[Udacity: Designing RESTful APIs](https://www.udacity.com/course/designing-restful-apis--ud388)|⬜|

|[Datacamp: Introduction to APIs in Python](https://www.datacamp.com/courses/introduction-to-apis-in-python)|⬜|

|[Udacity: Networking for Web Developers](https://www.udacity.com/course/networking-for-web-developers--ud256)|⬜|

### Production System Design

|Resource|Progress|

|---|---|

|[Book: Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)|✅|

|[Neetcode: System Design for Beginners](https://neetcode.io/courses/system-design-for-beginners/0)|✅|

|[Neetcode: System Design Interview](https://neetcode.io/courses/system-design-interview)|✅|

|[Datacamp: Customer Analytics & A/B Testing in Python](https://www.datacamp.com/courses/customer-analytics-ab-testing-in-python)|✅|

|[Datacamp: A/B Testing in Python](https://www.datacamp.com/courses/ab-testing-in-python)|⬜|

|[Udacity: A/B Testing](https://www.udacity.com/course/ab-testing--ud257)|⬜|

|[Datacamp: MLOps Concepts](https://www.datacamp.com/courses/mlops-concepts)|✅|

|[Datacamp: Machine Learning Monitoring Concepts](https://www.datacamp.com/courses/machine-learning-monitoring-concepts)|✅|

### Maths

	

|Resource|Progress|

|---|---|

|[Datacamp: Foundations of Probability in Python](https://www.datacamp.com/courses/foundations-of-probability-in-python)|✅|

|[Datacamp: Introduction to Statistics](https://www.datacamp.com/courses/introduction-to-statistics)|✅|

|[Datacamp: Introduction to Statistics in Python](https://www.datacamp.com/courses/introduction-to-statistics-in-python)|✅|

|[Datacamp: Hypothesis Testing in Python](https://www.datacamp.com/courses/hypothesis-testing-in-python)|✅|

|[Datacamp: Statistical Thinking in Python (Part 1)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1)|✅|

|[Datacamp: Statistical Thinking in Python (Part 2)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2)|✅|

|[Datacamp: Experimental Design in Python](https://datacamp.com/courses/experimental-design-in-python)|✅|

|[Datacamp: Practicing Statistics Interview Questions in Python](https://www.datacamp.com/courses/practicing-statistics-interview-questions-in-python)|⬜|

|[edX: Essential Statistics for Data Analysis using Excel](https://www.edx.org/course/essential-statistics-data-analysis-using-microsoft-dat222x-1)|✅|

|[Udacity: Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)|✅|

|[MIT 18.06 Linear Algebra, Spring 2005](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8)|✅|

|[Udacity: Eigenvectors and Eigenvalues](https://www.udacity.com/course/eigenvectors-and-eigenvalues--ud104)|✅|

|[Udacity: Linear Algebra Refresher](https://www.udacity.com/course/linear-algebra-refresher-course--ud953)|⬜|

|[Youtube: Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)|⬜|

### Basic Frontend Knowledge



### HTML

|Resource|Progress|

|---|---|

|[Codecademy: Learn HTML](https://www.codecademy.com/learn/learn-html)|✅|

|[Codecademy: Make a website](https://www.codecademy.com/en/courses/make-a-website)|✅|

|[Article: Alternative Text](https://webaim.org/techniques/alttext/)|⬜|

### CSS

|Resource|Progress|

|---|---|

|[Pluralsight: CSS Positioning](https://www.pluralsight.com/courses/css-positioning-1834)|✅|

|[Pluralsight: Introduction to CSS](https://www.pluralsight.com/courses/css-intro)|✅|

|[Pluralsight: CSS: Specificity, the Box Model, and Best Practices](https://app.pluralsight.com/interactive-courses/detail/c580b092-d94a-4ed8-8d2a-2f4d0b76f99f)|✅|

|[Pluralsight: CSS: Using Flexbox for Layout](https://app.pluralsight.com/interactive-courses/detail/a089d0a5-4a4c-4c4e-b883-c1bc64009619)|✅|

|[Code School: Blasting Off with Bootstrap](https://www.pluralsight.com/courses/code-school-blasting-off-with-bootstrap)|✅|

|[Pluralsight: UX Fundamentals](https://www.pluralsight.com/courses/ux-fundamentals-2426)|✅|

|[Codecademy: Learn SASS](https://www.codecademy.com/learn/learn-sass)|✅|

|[CSS for Javascript Developers](https://css-for-js.dev/)|✅|

|[Article: Create an illustration in Figma design](https://help.figma.com/hc/en-us/articles/13543867954711-Create-an-illustration-in-Figma-design)|✅|

|[Book: Refactoring UI](https://refactoringui.com/book/)|⬜|

|[Youtube: How to Make Your Website Not Ugly: Basic UX for Programmers](https://www.youtube.com/watch?v=Jf0cjocP8Wk)|⬜|

### JavaScript

|Resource|Progress|

|---|---|

|[Udacity: ES6 - JavaScript Improved](https://www.udacity.com/course/es6-javascript-improved--ud356)|✅|

|[Udacity: Intro to Javascript](https://www.udacity.com/course/intro-to-javascript--ud803)|✅|

|[Udacity: Object Oriented JS 1](https://www.udacity.com/course/object-oriented-javascript--ud015)|✅|

|[Udacity: Object Oriented JS 2](https://www.udacity.com/course/object-oriented-javascript--ud711)|✅|

|[Udemy: Understanding Typescript](https://www.udemy.com/understanding-typescript/)|✅|

|[Codecademy: Learn JavaScript](https://www.codecademy.com/learn/learn-javascript)|✅|

|[Codecademy: Jquery Track](https://www.codecademy.com/learn/learn-jquery)|✅|

|[Pluralsight: Using The Chrome Developer Tools](https://www.pluralsight.com/courses/chrome-developer-tools)|✅|

## Specialized Topics



### Machine Learning

|Resource|Progress|

|---|---|

|[Article: An overview of gradient descent optimization algorithms](https://www.ruder.io/optimizing-gradient-descent)|✅|

|[Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)|⬜|

|[Book: A Machine Learning Primer](https://www.confetti.ai/assets/ml-primer/ml_primer.pdf)|✅|

|[Book: Make Your Own Neural Network](https://www.amazon.com/Make-Your-Own-Neural-Network/dp/1530826608)|✅|

|[Book: Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning)|✅|

|[Book: The StatQuest Illustrated Guide To Machine Learning](https://www.amazon.com/StatQuest-Illustrated-Guide-Machine-Learning/dp/B0BLM4TLPY)|✅|

|[Fast.ai: Practical Deep Learning for Coder (Part 1)](https://course.fast.ai/)|✅|

|[Fast.ai: Practical Deep Learning for Coder (Part 2)](https://course.fast.ai/Lessons/part2.html)|⬜|

|[Datacamp: Ensemble Methods in Python](https://www.datacamp.com/courses/ensemble-methods-in-python)|✅|

|[Datacamp: Extreme Gradient Boosting with XGBoost](https://www.datacamp.com/courses/extreme-gradient-boosting-with-xgboost)|⬜|

|[Datacamp: Clustering Methods with SciPy](https://www.datacamp.com/courses/clustering-methods-with-scipy)|✅|

|[Datacamp: Unsupervised Learning in Python](https://www.datacamp.com/courses/unsupervised-learning-in-python)|✅|

|[Udacity: Segmentation and Clustering](https://www.udacity.com/course/segmentation-and-clustering--ud981)|✅|

|[Datacamp: Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)|✅|

|[edX: Implementing Predictive Analytics with Spark in Azure HDInsight](https://www.edx.org/course/implementing-predictive-analytics-spark-microsoft-dat202-3x-2)|✅|

|[Datacamp: Supervised Learning with scikit-learn](https://www.datacamp.com/courses/supervised-learning-with-scikit-learn)|✅|

|[Datacamp: Machine Learning with Tree-Based Models in Python](https://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python)|✅|

|[Datacamp: Linear Classifiers in Python](https://www.datacamp.com/courses/linear-classifiers-in-python)|✅|

|[Datacamp: Convolutional Neural Networks for Image Processing](https://www.datacamp.com/courses/convolutional-neural-networks-for-image-processing)|✅|

|[Datacamp: Model Validation in Python](https://www.datacamp.com/courses/model-validation-in-python)|✅|

|[Datacamp: Hyperparameter Tuning in Python](https://www.datacamp.com/courses/hyperparameter-tuning-in-python)|✅|

|[Datacamp: HR Analytics in Python: Predicting Employee Churn](https://www.datacamp.com/courses/hr-analytics-in-python-predicting-employee-churn)|✅|

|[Datacamp: Predicting Customer Churn in Python](https://www.datacamp.com/courses/predicting-customer-churn-in-python)|✅|

|[Datacamp: Dimensionality Reduction in Python](https://www.datacamp.com/courses/dimensionality-reduction-in-python)|✅|

|[Datacamp: Preprocessing for Machine Learning in Python](https://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python)|✅|

|[Datacamp: Data Types for Data Science](https://www.datacamp.com/courses/data-types-for-data-science)|✅|

|[Datacamp: Cleaning Data in Python](https://www.datacamp.com/courses/cleaning-data-in-python)|✅|

|[Datacamp: Feature Engineering for Machine Learning in Python](https://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python)|✅|

|[Datacamp: Predicting CTR with Machine Learning in Python](https://www.datacamp.com/courses/predicting-ctr-with-machine-learning-in-python)|✅|

|[Datacamp: Intro to Financial Concepts using Python](https://www.datacamp.com/courses/intro-to-financial-concepts-using-python)|✅|

|[Datacamp: Fraud Detection in Python](https://www.datacamp.com/courses/fraud-detection-in-python)|✅|

|[Karpathy: Neural Networks: Zero to Hero](https://github.com/karpathy/nn-zero-to-hero/)|✅|

|[Article: Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming](https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79)|⬜|

### Natural Language Processing

|Resource|Progress|

|---|---|

|[Book: Natural Language Processing with Transformers](https://transformersbook.com/)|✅|

|[Stanford CS224U: Natural Language Understanding \| Spring 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20)|✅|

|[Stanford CS224N: Stanford CS224N: NLP with Deep Learning \| Winter 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)|✅|

|[CMU: Low-resource NLP Bootcamp 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8A1CpCzURXAUa6H4HO7PF2c)|✅|

|[CMU Multilingual NLP 2020](http://demo.clab.cs.cmu.edu/11737fa20/)|✅|

|[Datacamp: Feature Engineering for NLP in Python](https://www.datacamp.com/courses/feature-engineering-for-nlp-in-python)|✅|

|[Datacamp: Natural Language Processing Fundamentals in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)|✅|

|[Datacamp: Regular Expressions in Python](https://www.datacamp.com/courses/regular-expressions-in-python)|✅|

|[Datacamp: RNN for Language Modeling](https://www.datacamp.com/courses/recurrent-neural-networks-for-language-modeling-in-python)|✅|

|[Datacamp: Natural Language Generation in Python](https://www.datacamp.com/courses/natural-language-generation-in-python)|✅|

|[Datacamp: Building Chatbots in Python](https://www.datacamp.com/courses/building-chatbots-in-python)|✅|

|[Datacamp: Sentiment Analysis in Python](https://www.datacamp.com/courses/sentiment-analysis-in-python)|✅|

|[Datacamp: Machine Translation in Python](https://www.datacamp.com/courses/machine-translation-in-python)|✅|

|[Article: The Unreasonable Effectiveness of Collocations](https://opensourceconnections.com/blog/2019/05/16/unreasonable-effectiveness-of-collocations/)|⬜|

|[Article: FuzzyWuzzy: Fuzzy String Matching in Python](https://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/#)|✅|

|[Article: Mamba Explained](https://thegradient.pub/mamba-explained/)|⬜|

|[Article: A Visual Guide to Mamba and State Space Models](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mamba-and-state)|⬜|

|[Article: Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/)|✅|

|[Article: Transformers: Origins](https://mark-riedl.medium.com/transformers-origins-1db4bdfcb3d1)|⬜|

### Generative AI



#### LLM Theory

|Resource|Progress|

|---|---|

|[Article: SolidGoldMagikarp (plus, prompt generation)](https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation)|⬜|

|[Book: Hands-On Large Language Models: Language Understanding and Generation](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961)|✅|

|[Book: Large Language Models: A Deep Dive: Bridging Theory and Practice](https://www.amazon.com/Large-Language-Models-Bridging-Practice/dp/3031656466)|⬜|

|[DeepLearning.AI: Pretraining LLMs](https://www.deeplearning.ai/short-courses/pretraining-llms)|✅|

|[DeepLearning.AI: How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/)|⬜|

|[Karpathy: Intro to Large Language Models](https://www.youtube.com/watch?v=zjkBMFhNj_g) [`1hr`]|✅|

|[Karpathy: Let's build the GPT Tokenizer](https://www.youtube.com/watch?v=zduSFxRajkE) [`2hr13m`]|✅|

|[Karpathy: Let's reproduce GPT-2 (124M)](https://www.youtube.com/watch?v=l8pRSuU81PU) [`4hr1m`]|⬜|

|[Youtube: A Hackers' Guide to Language Models](https://www.youtube.com/watch?v=jkrNMKz9pWU) [`1hr30m`]|✅|

|[Youtube: 5 Years of GPTs with Finbarr Timbers](https://www.youtube.com/watch?v=YA0pzBYAV2Q&list=PLKlhhkvvU8-YxMP9hjEYJTJDCaGszrJIh&index=8&t=43s)|⬜|

|[Article: Sampling for Text Generation](https://huyenchip.com/2024/01/16/sampling.html)|⬜|

|[DeepLearning.AI: Reinforcement Learning from Human Feedback](https://www.deeplearning.ai/short-courses/reinforcement-learning-from-human-feedback)|✅|

|[Youtube: LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU](https://www.youtube.com/watch?v=Mn_9W1nCFLo) [`1h10m`]|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (7): Prompting and Complex Reasoning](https://www.youtube.com/watch?v=1Faf1cTe3T8&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=2)|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (6): Instruction Tuning](https://www.youtube.com/watch?v=iWcGS0gCL1E&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=3)|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (12): Domain Specific Modeling: Code and Math](https://www.youtube.com/watch?v=qHNUVpKO2dc&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=4)|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (15): Tool Use and LLM Agent Basics](https://www.youtube.com/watch?v=a3SjRsqV9ZA&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=16)|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (14): Ensembling and Mixture of Experts](https://www.youtube.com/watch?v=E4Rg4qTw4xw&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=15)|⬜|

#### Multi-modality

|Resource|Progress|

|---|---|

|[Youtube: AI Visions Live \| Merve Noyan \| Open-source Multimodality](https://www.youtube.com/watch?v=_TlhKHTgWjY)|⬜|

|[DeepLearning.AI: Building Multimodal Search and RAG](https://www.deeplearning.ai/short-courses/building-multimodal-search-and-rag/)|⬜|

|[Article: Understanding Multimodal LLMs](https://magazine.sebastianraschka.com/p/understanding-multimodal-llms)|⬜|

#### Information Retrieval / RAG

| Resource                                                                                                                                       | Progress |

| ---------------------------------------------------------------------------------------------------------------------------------------------- | -------- |

| [Pretrained Transformer Language Models for Search - part 1](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-1/#) | ⬜        |

| [Pretrained Transformer Language Models for Search - part 2](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-2/)  | ⬜        |

| [Pretrained Transformer Language Models for Search - part 3](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-3)   | ⬜        |

| [Pretrained Transformer Language Models for Search - part 4](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-4)   | ⬜        |

| [Understanding LanceDB's IVF-PQ index](https://lancedb.github.io/lancedb/concepts/index_ivfpq/)                                                | ⬜        |

| [A little pooling goes a long way for multi-vector representations](https://www.answer.ai/posts/colbert-pooling.html)                          | ✅        |

| [Fullstack Retrieval Course](https://community.fullstackretrieval.com/)                                                                        |          |

|[Article: Levels of Complexity: RAG Applications](https://jxnl.github.io/blog/writing/2024/02/28/levels-of-complexity-rag-applications/)|✅|

|[Article: Systematically Improving Your RAG](https://jxnl.github.io/blog/writing/2024/05/22/systematically-improving-your-rag/)|⬜|

|[Article: Stop using LGTM@Few as a metric (Better RAG)](https://jxnl.github.io/blog/writing/2024/02/05/when-to-lgtm-at-k/)|⬜|

|[Article: Low-Hanging Fruit for RAG Search](https://jxnl.github.io/blog/writing/2024/05/11/low-hanging-fruit-for-rag-search/)|⬜|

|[Article: What AI Engineers Should Know about Search](https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search)|✅|

|[Article: Evaluating Chunking Strategies for Retrieval](https://research.trychroma.com/evaluating-chunking)|⬜|

|[Article: Sentence Embeddings. Introduction to Sentence Embeddings](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/)|⬜|

|[DeepLearning.AI: Building and Evaluating Advanced RAG Applications](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/)|✅|

|[DeepLearning.AI: Vector Databases: from Embeddings to Applications](https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/)|✅|

|[DeepLearning.AI: Advanced Retrieval for AI with Chroma](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/)|✅|

|[DeepLearning.AI: Prompt Compression and Query Optimization](https://www.deeplearning.ai/short-courses/prompt-compression-and-query-optimization/)|✅|

|[DeepLearning.AI: Large Language Models with Semantic Search](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search) [`1hr`]|✅|

|[DeepLearning.AI: Building Applications with Vector Databases](https://www.deeplearning.ai/short-courses/building-applications-vector-databases/)|✅|

|[DeepLearning.AI: Knowledge Graphs for RAG](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/)|⬜|

|[DeepLearning.AI: Functions, Tools and Agents with LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/)|⬜|

|[DeepLearning.AI: Building Agentic RAG with LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/)|⬜|

|[DeepLearning.AI: Multi AI Agent Systems with crewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/)|⬜|

|[DeepLearning.AI: AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/)|⬜|

|[DeepLearning.AI: AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)|⬜|

|[DeepLearning.AI: Building Your Own Database Agent](https://www.deeplearning.ai/short-courses/building-your-own-database-agent/)|⬜|

|[DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications](https://www.deeplearning.ai/short-courses/preprocessing-unstructured-data-for-llm-applications/)|⬜|

|[DeepLearning.AI: Embedding Models: From Architecture to Implementation](https://www.deeplearning.ai/short-courses/embedding-models-from-architecture-to-implementation)|✅|

|[Pinecone: Vector Databases in Production for Busy Engineers](https://www.pinecone.io/learn/series/vector-databases-in-production-for-busy-engineers/)|⬜|

|[Pinecone: Retrieval Augmented Generation](https://www.pinecone.io/learn/series/rag/)|⬜|

|[Pinecone: LangChain AI Handbook](https://www.pinecone.io/learn/series/langchain/)|⬜|

|[Pinecone: Embedding Methods for Image Search](https://www.pinecone.io/learn/series/image-search/)|⬜|

|[Pinecone: Faiss: The Missing Manual](https://www.pinecone.io/learn/series/faiss/)|⬜|

|[Pinecone: Vector Search in the Wild](https://www.pinecone.io/learn/series/wild/)|⬜|

|[Pinecone: Natural Language Processing for Semantic Search](https://www.pinecone.io/learn/series/nlp/)|⬜|

|[Youtube: Systematically improving RAG applications](https://youtu.be/RrDBV6odPKo?list=PLgIaq8VgndJvXkDSeReTl2u4rQMShkZ6V)|✅|

|[Youtube: Back to Basics for RAG w/ Jo Bergum](https://www.youtube.com/watch?v=nc0BupOkrhI&list=PLgIaq8VgndJvXkDSeReTl2u4rQMShkZ6V&index=2)|✅|

|[Youtube: Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié)](https://www.youtube.com/watch?v=0nA5QG3087g&t=1287s)|✅|

|[Youtube: RAG From Scratch](https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x)|0/14|

|[Article: LambdaMART in Depth](https://softwaredoug.com/blog/2022/01/17/lambdamart-in-depth)|⬜|

|[Article: Guided Generation with Outlines](https://medium.com/canoe-intelligence-technology/guided-generation-with-outlines-c09a0c2ce9eb)|✅|

|[Youtube: CMU Advanced NLP Fall 2024 (10): Retrieval and RAG](https://www.youtube.com/watch?v=KfQaYk4k9eM&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=6)|⬜|

#### Prompt Engineering

|Resource|Progress|

|---|---|

|[Article: OpenAI Prompt Engineering](https://platform.openai.com/docs/guides/prompt-engineering)|⬜|

|[Article: Prompting Fundamentals and How to Apply them Effectively](https://eugeneyan.com/writing/prompting/)|✅|

|[Anthropic Courses](https://github.com/anthropics/courses)|⬜|

|[Article: Prompt Engineering(Liliang Weng)](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)|✅|

|[Article: Prompt Engineering 201: Advanced methods and toolkits](https://amatria.in/blog/prompt201)|✅|

|[Article: Optimizing LLMs for accuracy](https://platform.openai.com/docs/guides/optimizing-llm-accuracy)|✅|

|[Article: Primers • Prompt Engineering](https://aman.ai/primers/ai/prompt-engineering/)|⬜|

|[Article: Anyscale Endpoints: JSON Mode and Function calling Features](https://www.anyscale.com/blog/anyscale-endpoints-json-mode-and-function-calling-features)|⬜|

|[Article: Guided text generation with Large Language Models](https://medium.com/productizing-language-models/guided-text-generation-with-large-language-models-d88fc3dcf4c)|⬜|

|[Article: GPT-4 Vision Alternatives](https://blog.roboflow.com/gpt-4-vision-alternatives/)|⬜|

|[DeepLearning.AI: ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)|⬜|

|[DeepLearning.AI: Prompt Engineering for Vision Models](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/)|⬜|

|[DeepLearning.AI: Prompt Engineering with Llama 2 & 3](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2/)|⬜|

|[Wandb: LLM Engineering: Structured Outputs](https://www.wandb.courses/courses/steering-language-models)|⬜|

|[DeepLearning.AI: Function-Calling and Data Extraction with LLMs](https://www.deeplearning.ai/short-courses/function-calling-and-data-extraction-with-llms/)|⬜|

|[Series: Prompt injection](https://simonwillison.net/series/prompt-injection/)|⬜|

|[Youtube: Prompt Engineering Overview](https://www.youtube.com/watch?v=dOxUroR57xs) [`1hr4m`]|✅|

|[Youtube: Structured Generation with LLMs](https://www.youtube.com/watch?v=KADrGwfSsEs&t=900s)|⬜|

#### LLMOps

|Resource|Progress|

|---|---|

|[Article: Patterns for Building LLM-based Systems & Products](https://eugeneyan.com/writing/llm-patterns/)|✅|

|[Article: Emerging Architectures for LLM Applications](https://a16z.com/emerging-architectures-for-llm-applications/)|✅|

|[Article: How to make LLMs go fast](https://vgel.me/posts/faster-inference/)|⬜|

|[Article: In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost](https://docs.titanml.co/blog/speculative-decoding-unleashed/)|⬜|

|[Article: Harmonizing Multi-GPUs: Efficient Scaling of LLM Inference](https://docs.titanml.co/blog/multi-gpu/)|⬜|

|[Article: Multi-Query Attention is All You Need](https://fireworks.ai/blog/multi-query-attention-is-all-you-need)|⬜|

|[Article: Transformers Inference Optimization Toolset](https://astralord.github.io/posts/transformer-inference-optimization-toolset/)|⬜|

|[DeepLearning.AI: Efficiently Serving LLMs](https://www.deeplearning.ai/short-courses/efficiently-serving-llms/)|✅|

|[DeepLearning.AI: Automated Testing for LLMOps](https://www.deeplearning.ai/short-courses/automated-testing-llmops/)|✅|

|[DeepLearning.AI: Red Teaming LLM Applications](https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/)|✅|

|[DeepLearning.AI: Evaluating and Debugging Generative AI Models Using Weights and Biases](https://www.deeplearning.ai/short-courses/evaluating-debugging-generative-ai/)|⬜|

|[DeepLearning.AI: Quality and Safety for LLM Applications](https://www.deeplearning.ai/short-courses/quality-safety-llm-applications/)|⬜|

|[DeepLearning.AI: LLMOps](https://www.deeplearning.ai/short-courses/llmops/)|⬜|

|[DeepLearning.AI: Serverless LLM apps with Amazon Bedrock](https://www.deeplearning.ai/short-courses/serverless-llm-apps-amazon-bedrock/)|⬜|

|[DeepLearning.AI: Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth/)|⬜|

|[DeepLearning.AI: Introduction to On-Device AI](https://www.deeplearning.ai/short-courses/introduction-to-on-device-ai/)|⬜|

|[Article: A Visual Guide to Quantization](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization)|⬜|

|[Article: QLoRA and 4-bit Quantization](https://mccormickml.com/2024/09/14/qlora-and-4bit-quantization/)|⬜|

|[Article: Understanding AI/LLM Quantisation Through Interactive Visualisations](https://smcleod.net/2024/07/understanding-ai/llm-quantisation-through-interactive-visualisations/)|⬜|

|[Youtube: CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning](https://www.youtube.com/watch?v=DvVGkj4zhVU&list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp&index=5)|⬜|

|[Article: LLM Inference Series: 3. KV caching explained](https://medium.com/@plienhar/llm-inference-series-3-kv-caching-unveiled-048152e461c8)|⬜|

|[Article: LLM Inference Series: 4. KV caching, a deeper look](https://medium.com/@plienhar/llm-inference-series-4-kv-caching-a-deeper-look-4ba9a77746c8)|⬜|

|[Article: LLM Inference Series: 5. Dissecting model performance](https://medium.com/@plienhar/llm-inference-series-5-dissecting-model-performance-6144aa93168f)|⬜|

|[Youtube: SBTB 2023: Charles Frye, Parallel Processors: Past & Future Connections Between LLMs and OS Kernels](https://www.youtube.com/watch?v=VxFtHqlMv8c)|⬜|

|[Article: Transformer Inference Arithmetic](https://kipp.ly/transformer-inference-arithmetic/)|⬜|

#### Building LLM-based Systems

|Resource|Progress|

|---|---|

|[Article: What We’ve Learned From A Year of Building with LLMs](https://applied-llms.org/)|⬜|

|[Article: How to Generate and Use Synthetic Data for Finetuning](https://eugeneyan.com/writing/synthetic/)|✅|

|[Article: Your AI Product Needs Evals](https://hamel.dev/blog/posts/evals)|✅|

|[Article: Task-Specific LLM Evals that Do & Don't Work](https://eugeneyan.com/writing/evals/)|✅|

|[Article: Data Flywheels for LLM Applications](https://www.sh-reya.com/blog/ai-engineering-flywheel/)|⬜|

|[Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy](https://www.godaddy.com/resources/news/llm-from-the-trenches-10-lessons-learned-operationalizing-models-at-godaddy#h-3-prompts-aren-t-portable-across-models)|✅|

|[Article: Evaluation & Hallucination Detection for Abstractive Summaries](https://eugeneyan.com/writing/abstractive/)|✅|

|[Article: Emerging UX Patterns for Generative AI Apps & Copilots](https://www.tidepool.so/blog/emerging-ux-patterns-for-generative-ai-apps-copilots)|✅|

|[Article: The Novice's LLM Training Guide](https://rentry.co/llm-training)|⬜|

|[Article: Pushing ChatGPT's Structured Data Support To Its Limits](https://minimaxir.com/2023/12/chatgpt-structured-data/)|✅|

|[Article: GPTed: using GPT-3 for semantic prose-checking](https://vgel.me/posts/gpted-launch/)|✅|

|[Article: Don't worry about LLMs](https://vickiboykis.com/2024/05/20/dont-worry-about-llms/)|⬜|

|[DeepLearning.AI: Finetuning Large Language Models](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/)|✅|

|[DeepLearning.AI: Building Systems with the ChatGPT API](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/)|⬜|

|[DeepLearning.AI: LangChain for LLM Application Development](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/)|⬜|

|[DeepLearning.AI: LangChain: Chat with Your Data](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/)|⬜|

|[DeepLearning.AI: Building Generative AI Applications with Gradio](https://www.deeplearning.ai/short-courses/building-generative-ai-applications-with-gradio/)|✅|

|[DeepLearning.AI: Open Source Models with Hugging Face](https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/)|⬜|

|[DeepLearning.AI: Getting Started with Mistral](https://www.deeplearning.ai/short-courses/getting-started-with-mistral/)|⬜|

|[Datacamp: Developing LLM Applications with LangChain](https://www.datacamp.com/courses/developing-llm-applications-with-langchain)|⬜|

|[LLMOps: Building with LLMs](https://www.comet.com/site/llm-course/)|⬜|

|[LLM Bootcamp - Spring 2023](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/)|✅|

|[Youtube: A Survey of Techniques for Maximizing LLM Performance](https://www.youtube.com/watch?v=ahnGLM-RC1Y)|✅|

|[Youtube: Building Blocks for LLM Systems & Products: Eugene Yan](https://www.youtube.com/watch?v=LzeC1AQ-U5o)|✅|

|[Youtube: Fine Tuning OpenAI Models - Best Practices](https://youtu.be/Q0GSZD0Na1s?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC)|✅|

|[Youtube: Course: LLM Fine-Tuning w/Axolotl](https://www.youtube.com/playlist?list=PLgIaq8VgndJuTUJgr-8khBZYigonBshUx)|0/4|

|[Youtube: Fine-Tuning LLMs](https://www.youtube.com/playlist?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC)|1/5|

|[Youtube: LLM Evals](https://www.youtube.com/playlist?list=PLgIaq8VgndJvt-HKMHPXehyJNNXQsAVHD)|0/5|

|[Youtube: Building LLM Applications](https://www.youtube.com/playlist?list=PLgIaq8VgndJtrxcelEdnXbvh9fXMHeAps)|0/8|

## Technical Skills (Libraries/Frameworks/Tools)

### AWS

|Resource|Progress|

|---|---|

|[Udemy: AWS Certified Developer - Associate 2018](https://www.udemy.com/aws-certified-developer-associate/)|✅|

### Django

|Resource|Progress|

|---|---|

|[Article: Django, HTMX and Alpine.js: Modern websites, JavaScript optional](https://www.saaspegasus.com/guides/modern-javascript-for-django-developers/htmx-alpine/)|✅|

### Matplotlib

|Resource|Progress|

|---|---|

|[Datacamp: Introduction to Seaborn](https://www.datacamp.com/courses/introduction-to-seaborn)|✅|

|[Datacamp: Introduction to Matplotlib](https://www.datacamp.com/courses/introduction-to-matplotlib)|✅|

### MLFlow

|Resource|Progress|

|---|---|

|[Datacamp: Introduction to MLFlow](https://www.datacamp.com/courses/introduction-to-mlflow)|✅|

### Nexxt.JS

| Resource                                                          | Progress |

| ----------------------------------------------------------------- | -------- |

| [Docs: Start building with Next.js](https://nextjs.org/learn) |          |

### Pandas

|Resource|Progress|

|---|---|

|[Datacamp: Pandas Foundations](https://www.datacamp.com/courses/pandas-foundations)|✅|

|[Datacamp: Pandas Joins for Spreadsheet Users](https://www.datacamp.com/courses/pandas-joins-for-spreadsheet-users)|✅|

|[Datacamp: Manipulating DataFrames with pandas](https://www.datacamp.com/courses/manipulating-dataframes-with-pandas)|✅|

|[Datacamp: Merging DataFrames with pandas](https://www.datacamp.com/courses/merging-dataframes-with-pandas)|✅|

|[Datacamp: Data Manipulation with pandas](https://www.datacamp.com/courses/data-manipulation-with-pandas)|✅|

|[Datacamp: Optimizing Python Code with pandas](https://www.datacamp.com/courses/optimizing-python-code-with-pandas)|✅|

|[Datacamp: Streamlined Data Ingestion with pandas](https://www.datacamp.com/courses/streamlined-data-ingestion-with-pandas)|✅|

|[Datacamp: Analyzing Marketing Campaigns with pandas](https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas)|✅|

|[Datacamp: Analyzing Police Activity with pandas](https://www.datacamp.com/courses/analyzing-police-activity-with-pandas)|✅|

### PyTorch

|Resource|Progress|

|---|---|

|[Article: PyTorch internals](https://blog.ezyang.com/2019/05/pytorch-internals/)|⬜|

|[Article: Taking PyTorch For Granted](https://nrehiew.github.io/blog/pytorch/)|⬜|

|[Datacamp: Introduction to Deep Learning with PyTorch](https://www.datacamp.com/courses/deep-learning-with-pytorch)|✅|

|[Datacamp: Intermediate Deep Learning with PyTorch](https://app.datacamp.com/learn/courses/intermediate-deep-learning-with-pytorch)|⬜|

|[Datacamp: Deep Learning for Text with PyTorch](https://www.datacamp.com/courses/deep-learning-for-text-with-pytorch)|⬜|

|[Datacamp: Deep Learning for Images with PyTorch](https://www.datacamp.com/courses/deep-learning-for-images-with-pytorch)|⬜|

|[Deeplizard: Neural Network Programming - Deep Learning with PyTorch](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG)|✅|

### ReactJS

|Resource|Progress|

|---|---|

|[Codecademy: Learn ReactJS: Part I](https://www.codecademy.com/learn/react-101)|✅|

|[Codecademy: Learn ReactJS: Part II](https://www.codecademy.com/learn/react-102)|✅|

|[NexxtJS: React Foundations](https://nextjs.org/learn/react-foundations)|⬜|

### Spacy

|Resource|Progress|

|---|---|

|[Datacamp: Advanced NLP with spaCy](https://www.datacamp.com/courses/advanced-nlp-with-spacy)|✅|

### Tensorflow & Keras

|Resource|Progress|

|---|---|

|[Datacamp: Introduction to TensorFlow in Python](https://www.datacamp.com/courses/introduction-to-tensorflow-in-python)|✅|

|[Datacamp: Deep Learning in Python](https://www.datacamp.com/courses/deep-learning-in-python)|✅|

|[Datacamp: Introduction to Deep Learning with Keras](https://www.datacamp.com/courses/deep-learning-with-keras-in-python)|✅|

|[Datacamp: Advanced Deep Learning with Keras](https://www.datacamp.com/courses/advanced-deep-learning-with-keras-in-python)|✅|

|[Deeplizard: Keras - Python Deep Learning Neural Network API](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)|✅|

|[Udacity: Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)|✅|