hgayan7/machine-learning-resources

Collection of links, tutorials and courses related to machine learning for easy reference.

Machine Learning Resources

Collection of links, tutorials and courses related to machine learning for easy reference.

Activation Functions

• Understanding Activation Functions in Neural Networks
• Activation Functions in Neural Networks (Sigmoid, tanh, Softmax, ReLU, Leaky ReLU)
• The Challenge of Vanishing/Exploding Gradients
• The Vanishing Gradient Problem
• Mish: Self Regularized Non-Monotonic Activation Function

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Weight Initialization

• Weight Initialization Techniques in Neural Networks (Zero, Random - He, Xavier)

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Optimizers

• Understanding All Optimizers In Deep Learning

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Loss Functions

• Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names

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Algorithms

• Naive Bayes Classifier
• Support Vector Machine (SVM) Basic Intuition - Video

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Regularization

• L1 and L2 Regularization Methods
• Intuitions on L1 and L2 Regularisation
• Elastic Net Regression - Video

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Dimensionality Reduction

• Multi-Dimension Scaling (MDS)
• Principal Component Analysis (PCA) - Video
• Principal Component Analysis - Explained Visually
• A One-Stop Shop for Principal Component Analysis
• StatQuest: t-SNE, Clearly Explained - Video
• How to Use t-SNE Effectively
• Understanding UMAP

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Boosting Algorithms

• AdaBoost Tutorial
• AdaBoost Classifier in Python
• Gradient Boosting from scratch
• Basic Ensemble Learning (Random Forest, AdaBoost, Gradient Boosting)- Step by Step Explained
• CatBoost overview
• What is XGBoost
• What is LightGBM, How to implement it? How to fine tune the parameters?

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Bayesian Networks

• Making Your Neural Network Say “I Don’t Know” — Bayesian NNs using Pyro and PyTorch
• Bayesian deep learning with Fastai : how not to be uncertain about your uncertainty !
• Bayesian Methods for Machine Learning - Coursera

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Active Learning

• Active Learning: Curious AI Algorithms
• modAL - Active Learning Framework

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Meta-Learning

• Learning to learn aka Meta-Learning - Curriculum
• MAML tensorflow implementation - Colab Notebook
• Model Agnostic Meta Learning - Video
• Explanation of One-shot Learning with Memory-Augmented Neural Networks

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CNN

• CS231n: Deep Learning for Computer Vision

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NLP

• An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec
• Paper Dissected: “Attention is All You Need” Explained
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
• Transformer Neural Networks - EXPLAINED! (Attention is all you need) - Video
• The Illustrated Transformer

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Normalization

• Box-Cox Transformations
• The Box-Cox Transformation

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Instance Segmentation

• Mask Region based Convolution Neural Networks - EXPLAINED! - Video

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Object Detection

• Region of interest pooling explained
• ROI pooling vs. ROI align
• Region Proposal Network (RPN)

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GAN

• Generative Adversarial Networks - Video Playlist
• Deep Generative Modeling | MIT 6.S191

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Reinforcement Learning

• Lectures from DeepMind research lead David Silver's course on reinforcement learning - Lecture Series
• Implementation of Reinforcement Learning Algorithms - Code, exercises and solutions
• A Free course in Deep Reinforcement Learning from beginner to expert - Video series
• Simple Reinforcement Learning with Tensorflow Part 0: Q-Learning with Tables and Neural Networks

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Recommender System

• Practical Recommender Systems - Book

Misc

• Jupyter Notebook Extensions
• PYRO - Deep Universal Probabilistic Programming
• Online/Incremental Learning with Keras and Creme
• CS 230 - Deep Learning Cheatsheet
• Stanford Machine Learning Notes
• Pretrained models CNNs
• Papers with Code
• colah's blog
• Distill
• MACHINE LEARNING SUMMER SCHOOL 2019 LONDON - Video lectures and slides