This repository is a personal collection of some useful Machine Learning resources like blogs, articles, research papers, course reviews etc for easy reference. Useful PRs are appreciated!

The following notes stem from the course 'Machine Learning' organized by Prof. Andrew Ng, Stanford at Coursera. They can serve as a good refresher of some basic topics in Machine Learning, apart from being a helpful aid in the course. You can also check out a week-wise distribution of these notes and the assignments at this link.

• Pattern Recognition and Machine Learning - Christopher M. Bishop
• Machine Learning: A Probabilistic perspective- Kevin P. Murphy
• The Elements of Statistical Learning
• Hands-On Machine Learning with Scikit-learn, Keras & Tensorflow

• An overview of gradient descent optimization algorithms
• In Depth: Gaussian Mixture Models
• Introduction to Explainable AI(XAI) using LIME
• Variational Autoencoders (VAEs) for Dummies
• Implement Deep Autoencoder in PyTorch for Image Reconstruction
• Visualizing the Bivariate Gaussian Distribution in Python
• How to set up and Run CUDA Operations in Pytorch ?
• Variational AutoEncoders (VAE) with PyTorch

• Machine Learning, Andrew Ng
• Machine Learning, Nando De Freitas
• CS224N: NLP with Deep Learning
• Probabilistic Machine Learning by Philipp Hennig
• MIT Introduction to Deep Learning
• Awesome explanation about GMMs