fucking-machine-learning-tutorials

Machine learning and deep learning tutorials, articles and other resources. With repository stars⭐ and forks🍴
https://github.com/correia-jpv/fucking-machine-learning-tutorials

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Uncategorized
- Uncategorized
Model Validation using Resampling
- Example of Bootstapping
- Resampling Explained
- Cross Validation
- How to use cross-validation in predictive modeling
- Good Resources
- Preventing Overfitting the Cross Validation Data | Andrew Ng
- Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- CV for detecting and preventing Overfitting
- Bootstrapping
- Partioning data set in R
- Implementing hold-out Validaion in R - data-frame-into-testing-and.html)
- Training with Full dataset after CV?
- Which CV method is best?
- Variance Estimates in k-fold CV
- Is CV a subsitute for Validation Set?
- Choice of k in k-fold CV
- CV for ensemble learning
- k-fold CV in R
- How does CV overcome the Overfitting Problem
- Why Bootstrapping Works?
- Understanding Bootstapping for Validation and Model Selection
- Cross Validation vs Bootstrap to estimate prediction error - validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)
Deep Learning
- Intro to CNNs
- GitHub Repo
- fast.ai - Cutting Edge Deep Learning For Coders
- A curated list of awesome Deep Learning tutorials, projects and communities
- Deep Learning Papers Reading Roadmap
- Interesting Deep Learning and NLP Projects (Stanford)
- Stanford Deep Learning Tutorial
- Recent Reddit AMAs related to Deep Learning
- Where to Learn Deep Learning?
- Deep Learning nvidia concepts
- Introduction to Deep Learning Using Python (GitHub) - to-deep-learning)
- Deep Learning Software List
- Hacker's guide to Neural Nets
- Awesome Deep Learning Reading List
- Deep Learning Comprehensive Website
- Deep Learning Basics
- Stanford Tutorials
- Deep Learning Tutorials on deeplearning.net
- Neural Networks and Deep Learning Online Book
- Machine Translation Reading List
- Torch vs. Theano
- Deep Learning Libraries by Language
- Theano
- Website
- Theano Tutorial
- Good Theano Tutorial
- Logistic Regression using Theano for classifying digits
- MLP using Theano
- CNN using Theano
- RNNs using Theano
- LSTM for Sentiment Analysis in Theano
- RBM using Theano
- DBNs using Theano
- All Codes
- Torch
- Torch ML Tutorial
- Intro to Torch
- Learning Torch GitHub Repo
- Awesome-Torch (Repository on GitHub)
- Torch Cheatsheet
- Understanding Natural Language with Deep Neural Networks Using Torch
- TensorFlow Examples for Beginners
- Simplified Scikit-learn Style Interface to TensorFlow
- Learning TensorFlow GitHub Repo
- Benchmark TensorFlow GitHub
- Awesome TensorFlow List
- TensorFlow Book
- GitHub Repo
- ANN implemented in C++ | AI Junkie
- NN for Beginners
- Regression and Classification with NNs (Slides)
- Another Intro
- awesome-rnn: list of resources (GitHub Repo)
- NLP RNN Representations
- The Unreasonable effectiveness of RNNs - rnn)), 🌎 [Python Code](gist.github.com/karpathy/d4dee566867f8291f086)
- Optimizing RNN Performance
- Simple RNN
- Music generation using RNNs (Keras)
- Using RNN to create on-the-fly dialogue (Keras)
- Understanding LSTM Networks
- Torch Code for character-level language models using LSTM
- LSTM for Kaggle EEG Detection competition (Torch Code)
- Deep Learning for Visual Q&A | LSTM | CNN - qa))
- Computer Responds to email using LSTM | Google
- LSTM dramatically improves Google Voice Search - short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/)
- Torch code for Visual Question Answering using a CNN+LSTM model
- Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
- Introduction to RBMs
- RBMs in R
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- Awesome Deep Vision: List of Resources (GitHub)
- Stanford Notes
- Deep learning to classify business photos at Yelp
- Awesome Graph Embedding
- Awesome Network Embedding
- Network Representation Learning Papers
- Knowledge Representation Learning Papers
- Graph Based Deep Learning Literature
- Lots of Deep Learning Resources
- Deep Learning nvidia concepts
- Top arxiv Deep Learning Papers explained
- Train, Validation & Test in Artificial Neural Networks
- Artificial Neural Networks Tutorials
- Neural Networks FAQs on Stack Overflow
- dl4j vs. torch7 vs. theano
- Theano Introduction
- Deep Learning Implementation Tutorials - Keras and Lasagne
- Website
- GitHub Repo
- Implementing a Neural Network from scratch - from-scratch))
- Role of Bias in Neural Networks
- Choosing number of hidden layers and nodes - layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#)
- Backpropagation in Matrix Form
- Simple Implementation
- NN for Beginners
- Recurrent Neural Net Tutorial Part 1 - neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), <b><code>     ?⭐</code></b> <b><code>     ?🍴</code></b> [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/))
- Intro to RNN
- An application of RNN
- Sequence Learning using RNN (Slides)
- Beginner’s Guide to LSTM
- Implementing LSTM from scratch - tutorial-gru-lstm))
- LSTM for Human Activity Recognition
- Recursive Neural Tensor Network (RNTN)
- word2vec, DBN, RNTN for Sentiment Analysis
- Beginner's Guide about RBMs
- Deep Belief Networks Tutorial
- Deep Autoencoders Tutorial
- GitHub Repo
- Machine Translation using RNN (Paper)
- Interview with Yann LeCun | Kaggle
Natural Language Processing
- word2vec explained on deeplearning4j
- A curated list of speech and natural language processing resources
- tf-idf explained
- Graph Based Semi Supervised Learning for NLP
- Classification text with Bag of Words
- **Probabilistic Topic Models Princeton PDF**
- **Introduction to LDA** - boyd-graber-and-philip-resnik.html)
- Online LDA - online-latent-dirichlet-allocation-with-apache-spark.html)
- LDA in Scala - dirichlet-allocation-in-scala-part-ii-the-code.html)
- Topic Modeling of Twitter Followers
- Multilingual Latent Dirichlet Allocation (LDA) - Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)))
- Deep Belief Nets for Topic Modeling
- Gaussian LDA for Topic Models with Word Embeddings
- Series of lecture notes for probabilistic topic models written in ipython notebook
- Implementation of various topic models in Python
- A closer look at Skip Gram Modeling
- Skip Gram Model Tutorial - tutorial-part-ii-the-continuous-bag-of-words-model.html)
- Language learning with NLP and reinforcement learning
Introduction
- AI/ML YouTube Courses
- An Introduction to Statistical Learning
- List of Machine Learning University Courses
- Machine Learning for Software Engineers
- Dive into Machine Learning
- A curated list of awesome Machine Learning frameworks, libraries and software
- A curated list of awesome data visualization libraries and resources.
- An awesome Data Science repository to learn and apply for real world problems
- The Open Source Data Science Masters
- Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
- Slides on Several Machine Learning Topics
- MIT Machine Learning Lecture Slides
- TheAnalyticsEdge edX Notes and Codes
- Have Fun With Machine Learning
- Twitter's Most Shared #machineLearning Content From The Past 7 Days
- Machine Learning FAQs on Cross Validated
- Slides on Several Machine Learning Topics
- Comparison Supervised Learning Algorithms
- Statistical Machine Learning Course
- In-depth introduction to machine learning in 15 hours of expert videos
- Learning Data Science Fundamentals
Decision Trees
- Probabilistic Trees Research Paper
- Pruning Decision Trees
- Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
- Good Article on comparison
- CART Explained
- Good Tutorial on CHAID
- Bayesian Learning in Probabilistic Decision Trees
- Probabilistic Trees Research Paper
- Thorough Explanation and different algorithms
Semi Supervised Learning
- Tutorial on Semi Supervised Learning
- Taxonomy
Artificial Intelligence
- Awesome Artificial Intelligence (GitHub Repo)
Genetic Algorithms
- Genetic Algorithms Explained in Plain English
- Genetic Programming in Python (GitHub)
Statistics
- Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
- Stat Trek Website - A dedicated website to teach yourselves Statistics
- What is a Sampling Distribution?
- AP Statistics Tutorial
- Statistics and Probability Tutorial
- Matrix Algebra Tutorial
- What are QQ Plots?
Useful Blogs
- Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
- ML Wave - A blog for Learning Machine Learning
- Andrej Karpathy - A blog about Deep Learning and Data Science in general
- Colah's Blog - Awesome Neural Networks Blog
- Statistically Significant - Andrew Landgraf's Data Science Blog
- fastML - Machine learning made easy
- A Quantitative Journey | outlace - learning quantitative applications
- Variance Explained - David Robinson's Blog
- AI Junkie - a blog about Artificial Intellingence
- Deep Learning Blog by Tim Dettmers - Making deep learning accessible
- J Alammar's Blog - Blog posts about Machine Learning and Neural Nets
- Ethen's Notebook Collection - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
- The Data School Blog - Data science for beginners!
- Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
- Simply Statistics - A blog by three biostatistics professors
- Trevor Stephens Blog - Trevor Stephens Personal Page
- r4stats - analyze the world of data science, and to help people learn to use R
- no free hunch | kaggle - The Kaggle Blog about all things Data Science
Kaggle Competitions WriteUp
- Facebook Recruiting III Explained
- Predicting CTR with Online ML
- Convolution Neural Networks for EEG detection
Cheat Sheets
- Probability Cheat Sheet
- Machine Learning Cheat Sheet
- Source
Linear Regression
- Assumptions of Linear Regression - is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
- Applying and Interpreting Linear Regression
Logistic Regression
- Geometric Intuition of Logistic Regression
- Guide to an in-depth understanding of logistic regression
Computer Vision
- Awesome computer vision (github)
Support Vector Machine
- A tutorial on SVMs
- Introductory Overview of SVMs
- Intro to SVM in R
- Classifier Classification with Platt's Scaling
Reinforcement Learning
- Awesome Reinforcement Learning (GitHub)
- RL Tutorial Part 1 - Learning-Part-2/)
Random Forest / Bagging
- Awesome Random Forest (GitHub)**
- Measures of variable importance in random forests
- Compare R-squared from two different Random Forest models
- Why doesn't Random Forest handle missing values in predictors?
- FAQs about Random Forest - forest)
- Obtaining knowledge from a random forest
Boosting
- Boosting Wikipedia Page
- Guidelines for GBM parameters in R - to-set-the-gbm-parameters)
- Meaning of Interaction Depth - does-interaction-depth-mean-in-gbm)
- Role of n.minobsinnode parameter of GBM in R
- GBM in R
- FAQs about GBM
- Practical XGBoost in Python online course (free)
- AdaBoost Sparse Input Support
- Tutorial
- Tutorial
- GitHub Project
- Boosting for Better Predictions
Ensembles
- Kaggle Ensembling Guide
- The Power of Simple Ensembles
- Ensemble Learning Intro
- Ensembling models with R - regression-models), [Intro to Ensembles in R](http://www.vikparuchuri.com/blog/intro-to-ensemble-learning-in-r/)
- Ensembling Models with caret
- Bagging vs Boosting vs Stacking
- Boosting vs Bagging
- Resources for learning how to implement ensemble methods
- How are classifications merged in an ensemble classifier?
Stacking Models
- Stacking, Blending and Stacked Generalization
- Stacked Generalization (Stacking)
- Stacked Generalization: when does it work?
- Stacked Generalization Paper
Vapnik–Chervonenkis Dimension
- Introduction to VC Dimension
- FAQs about VC Dimension
- Do ensemble techniques increase VC-dimension?
Bayesian Machine Learning
- Bayesian Methods for Hackers (using pyMC)
- Should all Machine Learning be Bayesian?
- Tutorial on Bayesian Optimisation for Machine Learning
- Bayesian Reasoning and Deep Learning - content/uploads/2015/10/Bayes_Deep.pdf)
- Kalman & Bayesian Filters in Python
Optimization
- Algorithms for Sparse Optimization and Machine Learning
- Optimization Algorithms for Data Analysis
- Video Lectures on Optimization
- The Interplay of Optimization and Machine Learning Research
- Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
Source
- ujjwalkarn/Machine-Learning-Tutorials
Classification
- ROC and AUC Explained - yo))
- Does Balancing Classes Improve Classifier Performance?
- What is Deviance?
- When to choose which machine learning classifier?
- Simple guide to confusion matrix terminology

Programming Languages

Python 9 Jupyter Notebook 8 Lua 3 TeX 3 R 2 Java 1 HTML 1

Categories

Deep Learning 111 Model Validation using Resampling 22 Introduction 21 Natural Language Processing 18 Useful Blogs 18 Boosting 12 Ensembles 9 Decision Trees 9 Statistics 7 Random Forest / Bagging 6 Classification 5 Bayesian Machine Learning 5 Uncategorized 5 Optimization 5 Stacking Models 4 Support Vector Machine 4 Vapnik–Chervonenkis Dimension 3 Kaggle Competitions WriteUp 3 Cheat Sheets 3 Genetic Algorithms 2 Logistic Regression 2 Reinforcement Learning 2 Linear Regression 2 Semi Supervised Learning 2 Source 1 Artificial Intelligence 1 Computer Vision 1

Sub Categories

Uncategorized 5

Keywords

machine-learning 12 deep-learning 10 awesome-list 5 data-science 5 python 5 awesome 4 neural-network 4 tensorflow 4 deep-learning-tutorial 3 natural-language-processing 3 artificial-intelligence 2 reinforcement-learning 2 clustering 2 tutorial 2 jupyter-notebook 2 torch 2 tensorflow-tutorials 2 ai 2 deep-neural-networks 2 deeplearning 2 neural-networks 2 caffe 1 knowledge-graph 1 image-classification 1 graph-embeddings 1 paper-list 1 examples 1 knowledge-embedding 1 graph-representation-learning 1 graph-neural-networks 1 graph-convolutional-networks 1 nlp 1 conference-publications 1 computer-science 1 courses 1 list 1 machinelearning 1 deep-networks 1 face-images 1 recurrent-networks 1 intelligent-machines 1 intelligent-systems 1 machine-intelligence 1 statistical-learning 1 unsupervised-learning 1 lists 1 resources 1 unicorns 1 bayesian-methods 1 mathematical-analysis 1