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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Introduction
- List of Machine Learning University Courses
- A curated list of awesome Machine Learning frameworks, libraries and software
- An Introduction to Statistical Learning
- The Open Source Data Science Masters
- Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
- Twitter's Most Shared #machineLearning Content From The Past 7 Days
- Have Fun With Machine Learning
- AI/ML YouTube Courses
- Machine Learning for Software Engineers
- A curated list of awesome data visualization libraries and resources.
- Dive into Machine Learning
- An awesome Data Science repository to learn and apply for real world problems
- Slides on Several Machine Learning Topics
- MIT Machine Learning Lecture Slides
- TheAnalyticsEdge edX Notes and Codes
- In-depth introduction to machine learning in 15 hours of expert videos
- Learning Data Science Fundamentals
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Source
-
Natural Language Processing
- A curated list of speech and natural language processing resources
- 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
- 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
- Multilingual Latent Dirichlet Allocation (LDA) - Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)))
- 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)
- 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
- word2vec explained on deeplearning4j
- Making sense of word2vec
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Computer Vision
-
Deep Learning
- A curated list of awesome Deep Learning tutorials, projects and communities
- Awesome TensorFlow List
- Awesome Deep Vision: List of Resources (GitHub)
- Torch
- fast.ai - Cutting Edge Deep Learning For Coders
- Website
- The Unreasonable effectiveness of RNNs - rnn)), š [Python Code](gist.github.com/karpathy/d4dee566867f8291f086)
- Understanding LSTM Networks
- Torch Cheatsheet
- Deep Learning Papers Reading Roadmap
- Recent Reddit AMAs related to Deep Learning
- Deep Learning Software List
- Awesome Deep Learning Reading List
- Deep Learning Comprehensive Website
- Deep Learning Basics
- Deep Learning Tutorials on deeplearning.net
- Theano
- 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
- Intro to Torch
- Learning TensorFlow GitHub Repo
- Benchmark TensorFlow GitHub
- Regression and Classification with NNs (Slides)
- Another Intro
- NLP RNN Representations
- Optimizing RNN Performance
- Simple RNN
- Using RNN to create on-the-fly dialogue (Keras)
- 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/)
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- Network Representation Learning Papers
- Where to Learn Deep Learning?
- Simplified Scikit-learn Style Interface to TensorFlow
- TensorFlow Examples for Beginners
- Graph Based Deep Learning Literature
- Knowledge Representation Learning Papers
- Awesome Network Embedding
- All Codes
- TensorFlow Book
- Awesome-Torch (Repository on GitHub)
- Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
- awesome-rnn: list of resources (GitHub Repo)
- Torch Code for character-level language models using LSTM
- Machine Translation Reading List
- Music generation using RNNs (Keras)
- LSTM for Kaggle EEG Detection competition (Torch Code)
- Torch code for Visual Question Answering using a CNN+LSTM model
- Introduction to Deep Learning Using Python (GitHub) - to-deep-learning)
- Awesome Graph Embedding
- Neural Networks and Deep Learning Online Book
- Interesting Deep Learning and NLP Projects (Stanford)
- Stanford Deep Learning Tutorial
- Deep Learning nvidia concepts
- Hacker's guide to Neural Nets
- Stanford Tutorials
- Torch vs. Theano
- Deep Learning Libraries by Language
- Torch ML Tutorial
- Learning Torch GitHub Repo
- Understanding Natural Language with Deep Neural Networks Using Torch
- GitHub Repo
- ANN implemented in C++ | AI Junkie
- NN for Beginners
- Deep Learning for Visual Q&A | LSTM | CNN - qa))
- Introduction to RBMs
- RBMs in R
- Stanford Notes
- Deep learning to classify business photos at Yelp
- 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))
- 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/), <b><code> ?ā</code></b> <b><code> ?š“</code></b> [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/))
- NN for Beginners
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Artificial Intelligence
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Useful Blogs
- ML Wave - A blog for Learning Machine Learning
- Colah's Blog - Awesome Neural Networks Blog
- Andrej Karpathy - A blog about Deep Learning and Data Science in general
- 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
- Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
- 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
- no free hunch | kaggle - The Kaggle Blog about all things Data Science
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Kaggle Competitions WriteUp
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Logistic Regression
- Geometric Intuition of Logistic Regression
- Guide to an in-depth understanding 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)
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Model Validation using Resampling
- Resampling Explained
- Cross Validation
- Good Resources
- CV for detecting and preventing Overfitting
- Bootstrapping
- How to use cross-validation in predictive modeling
- Preventing Overfitting the Cross Validation Data | Andrew Ng
- Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- Example of Bootstapping
-
Boosting
- Boosting Wikipedia Page
- Practical XGBoost in Python online course (free)
- Tutorial
- GitHub Project
- Tutorial
- 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
- AdaBoost Sparse Input Support
- Boosting for Better Predictions
- GBM in R
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Support Vector Machine
-
Reinforcement Learning
- RL Tutorial Part 1 - Learning-Part-2/)
- Awesome Reinforcement Learning (GitHub)
-
Decision Trees
- Pruning Decision Trees
- Good Article on comparison
- CART Explained
- Good Tutorial on CHAID
- Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
- 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?
- 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?
-
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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Stacking Models
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Bayesian Machine Learning
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Semi Supervised Learning
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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 Networksā Hyperparameters
- Optimization Algorithms in Machine Learning
- Mean Variance Portfolio Optimization with R and Quadratic Programming
- Video Lectures on Optimization
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Uncategorized
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Cheat Sheets
-
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
- How to build random forests in R with missing (NA) values?
- Some Questions for R implementation - preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact)
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Genetic Algorithms
-
Statistics
- Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
- What are QQ Plots?
- Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
-
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
- Linear Regression Comprehensive Resource
- 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?
-
VapnikāChervonenkis Dimension
-
Classification
- ROC and AUC Explained - yo))
Categories
Deep Learning
95
Decision Trees
22
Natural Language Processing
19
Introduction
17
Useful Blogs
13
Boosting
13
Ensembles
10
Linear Regression
10
Model Validation using Resampling
9
Optimization
8
Random Forest / Bagging
8
Support Vector Machine
7
Logistic Regression
6
Bayesian Machine Learning
6
Uncategorized
5
Genetic Algorithms
4
Semi Supervised Learning
4
Stacking Models
4
Kaggle Competitions WriteUp
3
VapnikāChervonenkis Dimension
3
Statistics
3
Artificial Intelligence
2
Reinforcement Learning
2
Cheat Sheets
2
Classification
1
Source
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
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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