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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Artificial Intelligence
Bayesian Machine Learning
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
Cheat Sheets
Classification
Computer Vision
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
- What is entropy and information gain in the context of building decision trees?
- 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
- 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, ?⭐ ?🍴 Code)
- Understanding CNN for NLP
- NN for Beginners
- Deep Learning nvidia concepts
- Sequence Learning using RNN (Slides)
- Train, Validation & Test in Artificial Neural Networks
- Artificial Neural Networks Tutorials
- Neural Networks FAQs on Stack Overflow
- 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
- Simple Implementation
Ensembles
- Kaggle Ensembling Guide
- The Power of Simple Ensembles
- Ensemble Learning Intro
- Ensembling models with R - regression-models), Intro to Ensembles 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
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
- In-depth introduction to machine learning in 15 hours of expert videos
- Learning Data Science Fundamentals
- Machine Learning FAQs on Cross Validated
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
- What does having constant variance in a linear regression model mean?
- Difference between linear regression on y with x and x with y
- Interpreting plot.lm() in R
- How to interpret a QQ plot?
- Interpreting Residuals vs Fitted Plot
- How should outliers be dealt with?