fucking-machine-learning-tutorials
Machine learning and deep learning tutorials, articles and other resources. With repository starsā and forksš“
https://github.com/correia-jpv/fucking-machine-learning-tutorials
Last synced: 7 days ago
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Artificial Intelligence
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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
- Should all Machine Learning be Bayesian?
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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
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Cheat Sheets
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Classification
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Computer Vision
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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?
- Slides Related to Decision Trees
- How to measure/rank āvariable importanceā 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?
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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)), š [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
- Beginnerās 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> ?ā</code></b> <b><code> ?š“</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)
- Lots of Deep Learning Resources
- Top arxiv Deep Learning Papers explained
- 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](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#)
- Backpropagation in Matrix Form
- Simple Implementation
- Intro to RNN
- An application of RNN
- Sequence Learning using RNN (Slides)
- Implementing LSTM from scratch - tutorial-gru-lstm))
- LSTM for Human Activity Recognition
- word2vec, DBN, RNTN for Sentiment Analysis
- Beginner's Guide about RBMs
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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
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Genetic Algorithms
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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
- Slides on Several Machine Learning Topics
- Comparison Supervised Learning Algorithms
Categories
Deep Learning
113
Decision Trees
26
Natural Language Processing
23
Model Validation using Resampling
22
Introduction
21
Useful Blogs
18
Support Vector Machine
16
Boosting
13
Ensembles
10
Statistics
8
Optimization
8
Random Forest / Bagging
8
Bayesian Machine Learning
7
Classification
5
Uncategorized
5
Stacking Models
4
Semi Supervised Learning
4
VapnikāChervonenkis Dimension
3
Linear Regression
3
Kaggle Competitions WriteUp
3
Genetic Algorithms
2
Reinforcement Learning
2
Logistic Regression
2
Cheat Sheets
2
Source
1
Artificial Intelligence
1
Computer Vision
1
Sub Categories
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