|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Tensorflow Examples||42,312||5 months ago||218||other||Jupyter Notebook|
|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)|
|100 Days Of Ml Code||40,344||9 days ago||61||mit|
|100 Days of ML Coding|
|100 Days Of Ml Code||17,892||a year ago||9||mit||Jupyter Notebook|
|Recommenders||15,284||2||15 hours ago||11||April 01, 2022||150||mit||Python|
|Best Practices on Recommendation Systems|
|Awesome Pytorch List||13,786||a month ago||2|
|A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.|
|Machine Learning Tutorials||12,876||3 months ago||33||cc0-1.0|
|machine learning and deep learning tutorials, articles and other resources|
|Stanford Tensorflow Tutorials||10,215||2 years ago||88||mit||Python|
|This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.|
|Mit Deep Learning||9,328||5 months ago||15||mit||Jupyter Notebook|
|Tutorials, assignments, and competitions for MIT Deep Learning related courses.|
|Computervision Recipes||8,817||2 months ago||65||mit||Jupyter Notebook|
|Best Practices, code samples, and documentation for Computer Vision.|
|Tensorflow Tutorials||8,644||2 years ago||2||mit||Jupyter Notebook|
|TensorFlow Tutorials with YouTube Videos|
This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.
If you want to contribute to this list, please read Contributing Guidelines.
Curated list of R tutorials for Data Science, NLP and Machine Learning.
Curated list of Python tutorials for Data Science, NLP and Machine Learning.
In-depth introduction to machine learning in 15 hours of expert videos
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
Machine Learning algorithms that you should always have a strong understanding of
Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
Twitter's Most Shared #machineLearning Content From The Past 7 Days
41 Essential Machine Learning Interview Questions (with answers)
How can a computer science graduate student prepare himself for data scientist interviews?
Programming Community Curated Resources for learning Artificial Intelligence
MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course
Simple Implementation of Genetic Algorithms in Python (Part 1), Part 2
Stat Trek Website - A dedicated website to teach yourselves Statistics
Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
Statistics for Hackers | Slides | @jakevdp - Slides by Jake VanderPlas
Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
OpenIntro Statistics - Free PDF textbook
Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
The Data School Blog - Data science for beginners!
ML Wave - A blog for Learning Machine Learning
Andrej Karpathy - A blog about Deep Learning and Data Science in general
Colah's Blog - Awesome Neural Networks Blog
Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
Statistically Significant - Andrew Landgraf's Data Science Blog
Simply Statistics - A blog by three biostatistics professors
Yanir Seroussi's Blog - A blog about Data Science and beyond
fastML - Machine learning made easy
Trevor Stephens Blog - Trevor Stephens Personal Page
no free hunch | kaggle - The Kaggle Blog about all things Data Science
A Quantitative Journey | outlace - learning quantitative applications
r4stats - analyze the world of data science, and to help people learn to use R
Variance Explained - David Robinson's Blog
AI Junkie - a blog about Artificial Intellingence
Deep Learning Blog by Tim Dettmers - Making deep learning accessible
J Alammar's Blog- Blog posts about Machine Learning and Neural Nets
Adam Geitgey - Easiest Introduction to machine learning
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
What are the advantages of different classification algorithms?
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?
Multicollinearity and VIF
Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
Pseudo R2 for Logistic Regression, How to calculate, Other Details
Overfitting and Cross Validation
A curated list of awesome Deep Learning tutorials, projects and communities
Interesting Deep Learning and NLP Projects (Stanford), Website
Understanding Natural Language with Deep Neural Networks Using Torch
Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides
Video Lectures Oxford 2015, Video Lectures Summer School Montreal
Neural Machine Translation
Deep Learning Frameworks
Feed Forward Networks
Speeding up your Neural Network with Theano and the gpu, Code
The Unreasonable effectiveness of RNNs, Torch Code, Python Code
Long Short Term Memory (LSTM)
LSTM dramatically improves Google Voice Search, Another Article
Torch code for Visual Question Answering using a CNN+LSTM model
Gated Recurrent Units (GRU)
Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
Restricted Boltzmann Machine
Autoencoders: Unsupervised (applies BackProp after setting target = input)
Convolutional Neural Networks
Network Representation Learning
A curated list of speech and natural language processing resources
Understanding Natural Language with Deep Neural Networks Using Torch
What is a good explanation of Latent Dirichlet Allocation (LDA)?
Multilingual Latent Dirichlet Allocation (LDA). (Tutorial here)
Named Entity Recognitation
Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
Probabilities post SVM
What is entropy and information gain in the context of building decision trees?
How do decision tree learning algorithms deal with missing values?
Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
Comparison of Different Algorithms
Probabilistic Decision Trees
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
Why doesn't Random Forest handle missing values in predictors?
Gradient Boosting Machine
Ensembling models with R, Ensembling Regression Models in R, Intro to Ensembles in R
Mean Variance Portfolio Optimization with R and Quadratic Programming
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters