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

Last synced: 15 days ago
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Artificial Intelligence
- Awesome Artificial Intelligence (GitHub Repo)
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
- Bayesian Statistics Made Simple
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
- GBM in R
Cheat Sheets
- Probability Cheat Sheet
- Machine Learning Cheat Sheet
Classification
- ROC and AUC Explained - yo))
Computer Vision
- Awesome computer vision (github)
Decision Trees
- 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
- Probabilistic Trees Research Paper
- FAQs about Decision Trees
- Weak side of Decision Trees
- How do decision tree learning algorithms deal with missing values?
- Are decision trees almost always binary trees?
- CART vs CTREE
- CHAID vs CART - trees-cart-vs-chaid.html)
- How Decision Trees work?
- Slides Related to Decision Trees
- Tree Based Models in R
- How to measure/rank “variable importance” when using CART?
- Pruning a Tree in R
- Does rpart use multivariate splits by default?
- FAQs about Recursive Partitioning
- Show volumne in each node using ctree in R
- How to extract tree structure from ctree function?
Deep Learning
- 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
- GitHub Repo
- Machine Translation using RNN (Paper)
- Interview with Yann LeCun | Kaggle
- Intro to CNNs
- GitHub Repo
- 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
- deeplearning Tutorials
- dl4j vs. torch7 vs. theano
- Theano Introduction
- Implementing a Neural Network from scratch - from-scratch))
- 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/))
- Understanding CNN for NLP
- NN for Beginners
- Deep Learning nvidia concepts
- Sequence Learning using RNN (Slides)
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?
- Ensemble Learning Paper
Genetic Algorithms
- Genetic Algorithms Explained in Plain English
- Genetic Programming in Python (GitHub)
Introduction
Kaggle Competitions WriteUp
Linear Regression
- 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
- Geometric Intuition of Logistic Regression
- Guide to an in-depth understanding of logistic regression
Model Validation using Resampling
Natural Language Processing
- 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

Programming Languages

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

Categories

Deep Learning 97 Decision Trees 24 Natural Language Processing 19 Introduction 17 Useful Blogs 13 Boosting 13 Ensembles 10 Model Validation using Resampling 9 Support Vector Machine 9 Random Forest / Bagging 8 Optimization 6 Bayesian Machine Learning 6 Uncategorized 5 Stacking Models 4 Linear Regression 3 Vapnik–Chervonenkis Dimension 3 Kaggle Competitions WriteUp 3 Statistics 2 Genetic Algorithms 2 Reinforcement Learning 2 Semi Supervised Learning 2 Cheat Sheets 2 Logistic Regression 2 Source 1 Artificial Intelligence 1 Computer Vision 1 Classification 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