- General
- Generative Adversarial Network (GAN)
- Genetic Algorithms
- RNN
- Natural Language Processing (NLP)
- Computer Vision
- Data Science
- Machine Learning
- Programming in R
- Visualisation
- More...
- Contributing
- Python Toolkit Used for Two Kaggle Top 10% Leaderboard Positions
- Jeff Heaton's Kaggle boosting work
- An example on how-to install a Java kernel for JuPyteR notebooks (Graal enabled, where platform supports)
- NeptuneML GitHub repo | NeptuneML - Miverna training material, hint: Kaggle competition booster
- Large-scale linear classification, regression and ranking in Python
- AutoKeras: The Killer of Google’s AutoML
- A curated list of research, applications and projects built using H2O Machine Learning | H2O GitHub org
- Tutorial: Advanced Jupyter Notebooks
- Scipy Lecture Notes: One document to learn numerics, science, and data with Python
- Web Scraping - It’s Your Civic Duty
- Python for Computational Science and Engineering (book)
- PyCon 2015 Scikit-learn Tutorial
- Book: Automate the boring stuff
- Book: How to Think Like a Computer Scientist: Interactive Edition | How to Think Like a Computer Scientist - non-Interactive Edition [deadlink]
- Python Project (Classification):
- Part A: https://www.youtube.com/watch?v=p0snNMCbvN4&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=8
- Part B: https://www.youtube.com/watch?v=j4IgXflsZtg&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=9
- Part C: https://www.youtube.com/watch?v=kHZmFVDm0QQ&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=10
- A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R
- ⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐
- ⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐
- Have You Heard About Unsupervised Decision Trees
- Detecting Money Laundering with Unsupervised ML
- k-nearest neighbor algorithm using Python
- KMeans and Elbow discussion
- Clustering with non numeric data
- Have u ever heard about Bounded Clustering?
- Unsupervised Learning Techniques
- How To Train Interpretable Neural Networks That Accurately Extrapolate From Small Data By Christopher Rackauckas | LinkedIn
- How to generate neural network confidence intervals with Keras
- A pair of interrelated neural networks in DQN by Rafael Stekolshchik LinkedIn Post
- How to Visualize Filters and Feature Maps in Convolutional Neural Networks
- Understand the Impact of Learning Rate on Neural Network Performance
- Neural Module Networks for Reasoning over Text: [Paper] | Code LinkedIn Post
- ”A Beginner's Guide to the Mathematics of Neural Networks” LinkedIn Post
- Machine Learning: Data Insights for Model Building LinkedIn Post
- Representation of NN with different variants
- Neural Networks are Function Approximation Algorithms
- Troubleshooting Deep NNs
- Understand the Impact of Learning Rate on Neural Network Performance
- Andrej Karpathy’s blog post: Neural Network recipes
- Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2
- A Beginner's Guide to Generative Adversarial Networks (GANs)
- A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
- Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow
- A probabilistic programming language in TensorFlow. Deep generative models, variational inference
- Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
- [GAN Playground](https://reiinakano.com/gan-playground/
- Introduction to Genetic Algorithms & their application in data science [deadlink]
- Hands-On Genetic Algorithms with Python book:
- Genetic Algorithm: The Nature of Code playlist
- Session 2 - Genetic Algorithms - Intelligence and Learning The Coding Train
- Coding Challenge #35.4: Traveling Salesperson with Genetic Algorithm
- Coding Challenge #35.5: TSP with Genetic Algorithm and Crossover
- 11.2: Neuroevolution: Crossover and Mutation - The Nature of Code
- Live Stream #52: Genetic Algorithms
- 11.1: Introduction to Neuroevolution - The Nature of Code
- Live Stream #54 - Phyllotaxis and More on Genetic Algorithms
- Coding Challenge #133: Times Tables Cardioid Visualization
- Coding Challenge #125: Fourier Series
- What is Computer vision?
- Main topics to be covered (Basic outline)
- Introduction to CV
- Another: Introduction to CV
- Digital Image Processing Basics
- Theory for Image Processing
- Object Instance Segmentation
- Object Tracking
- See Image Processing in Courses
See Computer Vision
- PyImagesearch by Adrian Rosebrock
- Tombone's Computer Vision Blog
- Get started in Computer Vision, Quora
- The Data Science Process
- Also see Data Science
- Data science intro for math/phys background by Poitr Migdal
- What I do or: science to data science by Poitr Migdal
- T. Cormen, C. Leiserson, R. Rivest and C. Stein, Introduction to Algorithms
- David MacKay, Information Theory, Inference, and Learning Algorithms
- Virgilio - Your new Mentor for Data Science E-Learning
- Introduction to my data science book by Vincent Granville
- Top 10 Data Science Myths
- Explanation of most popular Data Science Library (in Python)
- History of Data Science
- Top 10 Data Science Terminology
- Data Science Hand book that helps the newbie to guide toward the new wings of the era Data Science
- Data Science In One Pictures
- Data Science complete PDF
- Data Science Cheatsheet
- FREE #AI/ #DataScience/ #MachineLearning CHEAT SHEETS courtesy of Stanford University!
- All in One Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
- Data Science Lifecycle
- 50 most popular Python libraries and frameworks used in data science
- Data Science Process by Nabih Bawazir
- Forecasting through ARIMA Modeling using R – Step-by-step Guide
- 📌50 Days of Machine Learning📌
- 𝐄𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐡𝐚𝐯𝐞 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐜𝐨𝐧𝐜𝐞𝐩𝐭
- 𝐓𝐡𝐞 𝟐𝟎 𝐁𝐞𝐬𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐁𝐨𝐨𝐤𝐬 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐨𝐧𝐥𝐢𝐧𝐞 𝐢𝐧 𝟐𝟎𝟐𝟎. 𝐀𝐥𝐥 𝐁𝐨𝐨𝐤'𝐬 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐥𝐢𝐧𝐤 is 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐢𝐧 𝐛𝐞𝐥𝐨𝐰 𝐥𝐢𝐧𝐤 LinkedIn Post
- 20 short tutorials all data scientists should read (and practice) LinkedIn Post
- MUST READ ARTICLES FOR DATA SCIENCE ENTHUSIAST
- Refined Data is the Key to Train Machine Learning LinkedIn Post
- Here are some great Python Resources to learn #DataScience and #MachineLearning
- Best Python Libraries for DS LinkedIn Post
- Question: Tool framework for starting data science with Python LinkedIn Post
- The technique of learning data science LinkedIn Post
- 10 Books Data Scientists Should Read During Lockdown LinkedIn Post
- Free Books: Data Science & AI
- 50 external machine learning / data science resources and articles
- Bayesian Stats 101 for Data Scientists
- Statistics formula for Data Science
- See Algorithms under Courses
See Machine Learning
- A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time
- Testing Machine Learning Models with Eric Schles
Also see Machine Learning > Testing
See Deep Learning
- Debugging in R
- ANOVA Test in R Programming
- Survival Analysis in R
- Using ggplot2 in R
- Principal component analysis in R
- Working with Excel files
- Logistic Regression in R
- Random Forest Approach in R Programming
- K-NN Classifier in R programming
- Naive Bayes Classifier in R programming
- Association Rule Mining in R programming
- Lasso Regression in R programming
- Ridge Regression in R programming
- Elastic Net Regression in R programming
- Quantile Regression in R
- LOOCV (Leave One Out Cross-Validation) in R Programming
See Visualisation
- Julia: See this link for more Julia related ML links
- Python: See this link for more Python related ML links
- R: See this link for more R related ML links
- Top 7 Libraries and Packages for Data Science and AI: Python & R
- PyTorch
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