Machine-Learning-Tutorials

machine learning and deep learning tutorials, articles and other resources
https://github.com/ujjwalkarn/Machine-Learning-Tutorials

Last synced: 15 days ago
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Deep Learning
- Implementing a Neural Network from scratch - from-scratch)
- Basic ANN Theory
- 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
- ANN implemented in C++ | AI Junkie
- Simple Implementation
- NN for Beginners
- Regression and Classification with NNs (Slides)
- Another Intro
- 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/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/)
- NLP RNN Representations
- The Unreasonable effectiveness of RNNs - rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086)
- An application of RNN
- Optimizing RNN Performance
- Simple RNN
- Auto-Generating Clickbait with RNN
- Machine Translation using RNN (Paper)
- Using RNN to create on-the-fly dialogue (Keras)
- Understanding LSTM Networks
- LSTM explained
- LSTM vs GRU
- 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/)
- Recursive Neural Network (not Recurrent)
- Introduction to RBMs
- Hinton's Guide to Training RBMs
- Andrew Ng Sparse Autoencoders pdf
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- An Intuitive Explanation of Convolutional Neural Networks
- Understanding CNN for NLP
- Stanford Notes
- Using CNNs to detect facial keypoints
- Visualising and Understanding CNNs
- Network Representation Learning Papers
- Where to Learn Deep Learning?
- Sequence Learning using RNN (Slides)
- Deep Learning Implementation Tutorials - Keras and Lasagne
- Interesting Deep Learning and NLP Projects (Stanford)
- Stanford Deep Learning Tutorial
- Deep Learning nvidia concepts
- Introduction to Deep Learning Using Python (GitHub) - to-deep-learning)
- Hacker's guide to Neural Nets
- Stanford Tutorials
- Neural Networks and Deep Learning Online Book
- Machine Translation Reading List
- Torch vs. Theano
- Deep Learning Libraries by Language
- All Codes
- Torch ML Tutorial
- Learning Torch GitHub Repo
- Awesome-Torch (Repository on GitHub)
- Understanding Natural Language with Deep Neural Networks Using Torch
- TensorFlow Examples for Beginners
- Simplified Scikit-learn Style Interface to TensorFlow
- TensorFlow Book
- GitHub Repo
- GitHub Repo
- ANN implemented in C++ | AI Junkie
- NN for Beginners
- awesome-rnn: list of resources (GitHub Repo)
- Music generation using RNNs (Keras)
- 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)
- 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
- Stanford Notes
- Deep learning to classify business photos at Yelp
- Awesome Graph Embedding
- Awesome Network Embedding
- Knowledge Representation Learning Papers
- Graph Based Deep Learning Literature
- Machine Translation using RNN (Paper)
- Interview with Yann LeCun | Kaggle
- Intro to CNNs
- Visualising and Understanding CNNs
- GitHub Repo
- deeplearning Tutorials
- Deep Learning Implementation Tutorials - Keras and Lasagne
- GitHub Repo
- Beginner’s Guide to LSTM
- Recursive Neural Tensor Network (RNTN)
- Deep Belief Networks Tutorial
- Deep Autoencoders Tutorial
- dl4j vs. torch7 vs. theano
- Theano Introduction
- Implementing a Neural Network from scratch - from-scratch)
- Understanding CNN for NLP
- 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/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/)
- NN for Beginners
- Deep Learning nvidia concepts
- Sequence Learning using RNN (Slides)
- Core Concepts of Deep Learning
- Understanding Natural Language with Deep Neural Networks Using Torch
- Introduction to Neural Machine Translation with GPUs (part 1) - neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/)
- Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning
- Deep Learning for Computer Vision with Caffe and cuDNN
- Basic ANN Theory
Ensembles
- Wikipedia Article on Ensemble Learning
- Kaggle Ensembling Guide
- The Power of Simple Ensembles
- Ensemble Learning Intro
- Ensemble Learning Paper
- 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
- Good Resources | Kaggle Africa Soil Property Prediction
- Boosting vs Bagging
- Resources for learning how to implement ensemble methods
- How are classifications merged in an ensemble classifier?
- 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?
- Ensemble Learning Paper
- Good Resources | Kaggle Africa Soil Property Prediction
Genetic Algorithms
- Genetic Algorithms Wikipedia Page
- Simple Implementation of Genetic Algorithms in Python (Part 1)
- Genetic Algorithms vs Artificial Neural Networks
- Genetic Algorithms Explained in Plain English
- Genetic Programming
- Genetic Alogorithms vs Genetic Programming (Quora) - are-the-differences-between-genetic-algorithms-and-genetic-programming)
- Genetic Algorithms Explained in Plain English
- Genetic Programming in Python (GitHub)
Interview Resources
Introduction
Kaggle Competitions WriteUp
Linear Regression
- Assumptions of Linear Regression - is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
- Linear Regression Comprehensive Resource
- Applying and Interpreting Linear Regression
- What does having constant variance in a linear regression model mean?
- Difference between linear regression on y with x and x with y
- Is linear regression valid when the dependant variable is not normally distributed?
- Dummy Variable Trap | Multicollinearity
- Dealing with multicollinearity using VIFs
- Interpreting plot.lm() in R
- How to interpret a QQ plot?
- Interpreting Residuals vs Fitted Plot
- How should outliers be dealt with?
- Elastic Net
- Assumptions of Linear Regression - is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
- Applying and Interpreting Linear Regression
- Interpreting plot.lm() in R
Logistic Regression
- Logistic Regression Wiki
- Geometric Intuition of Logistic Regression
- Obtaining predicted categories (choosing threshold)
- Residuals in logistic regression
- Difference between logit and probit models
- Pseudo R2 for Logistic Regression - to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm)
- Guide to an in-depth understanding of logistic regression

Programming Languages

Python 10 Jupyter Notebook 8 Lua 3 TeX 3 R 2 Java 1 HTML 1

Categories

Deep Learning 154 Natural Language Processing 88 Boosting 65 Decision Trees 52 Model Validation using Resampling 40 Introduction 30 Support Vector Machine 30 Useful Blogs 25 Ensembles 21 Random Forest / Bagging 18 Linear Regression 16 Optimization 13 Statistics 13 Resources on Quora 10 Bayesian Machine Learning 10 Vapnik–Chervonenkis Dimension 9 Artificial Intelligence 8 Genetic Algorithms 8 Kaggle Competitions WriteUp 8 Semi Supervised Learning 8 Logistic Regression 8 Interview Resources 7 Classification 7 Stacking Models 6 Cheat Sheets 5 Uncategorized 3 Reinforcement Learning 2 Computer Vision 2

Sub Categories

Uncategorized 3

Keywords

machine-learning 11 deep-learning 9 python 5 data-science 5 awesome-list 4 tensorflow 4 natural-language-processing 3 neural-network 3 awesome 3 tutorial 2 ai 2 artificial-intelligence 2 clustering 2 reinforcement-learning 2 tensorflow-tutorials 2 jupyter-notebook 2 deep-learning-tutorial 2 torch 2 machine-learning-algorithms 1 representation-learning 1 nlp 1 conference-publications 1 graph 1 graph-convolutional-networks 1 graph-neural-networks 1 graph-representation-learning 1 neural-networks 1 knowledge-embedding 1 paper-list 1 graph-embeddings 1 network-embedding 1 knowledge-graph 1 computer-science 1 courses 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 pymc 1 statistics 1 caffe 1