• The resources include here***:

1. Free machine and deep learning books.

2. M.Sc /Ph.D programmes in Artificial Intelligence.

3. Free online deep learning courses.

Deep Learning Videos

• PRML algorithms implemented in Python

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• Machine Learning from scratch! and benchmarking against popular ML library, by Quan Tran: https://lnkd.in/er_ZNgY

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• Python Data Science Handbook, with example

• The Most Complete Python Book (Beginner to Pro)

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• Over 200 of the Best Machine Learning, NLP, and Python Tutorials****

• ALL Data-Science--Cheat-Sheet By ASIF Bhat **************

• Visualization with Matplotlib ******

• Python Data Science Handbook *****

• Python 3 Exercices corrigés ***

• Exercices corrigés***

• Linear Regression in Python

• python-graph-gallery

• Coursera Deep Learning courses , RESUME by hand

• Reinforcement Learning by Richard S. Sutton and Andrew G. Barto

• kNN Machine Learning Algorithm

• Machine learning - les briques de bases

• Ref Book : Deep Learning, An MIT Press book, I. Goodfellow, Y. Bengio and A. Courville

• Machine Learning , Slides, Labs with R by Lucas Leemann, Essex Summer School

• More than 9000 papers with large number of data-sets and code**

• 40 Questions -solution to test a data scientist on Machine Learning

• More and more ML interview Questions by Ankit Gupta

• ARTIFICIAL INTELLIGENCE RESOURCES ***

• All Data Science ***Cheat Sheets*** in one place.

• Dive into Deep Learning

• Deep Learning Drizzle

• Polya-Based Approximation for the ATM-Forward Implied Volatility , cpp

• A birds-eye view of optimization algorithms

• The Matrix Calculus You Need For Deep Learning

• PCA, GMM, EM , Sebastian Jaimungal, University of Toronto

• Linear classifiers (1): Basics

• Papers with Code***: https://paperswithcode.com

• amuguruza/NN-StochVol-Calibrations**

• Stanford Machine Learning , by Professor Andrew Ng

• Originally posted on the ml-class.org

• Deep hedging

• Neural networks for stock price prediction

• Course schedule: https://lnkd.in/eDW7FTs

• Lecture 1MIT:Introduction to Deep Learning

• Lecture 2MIT: Recurrent Neural Networks

• Tensorflow and deep learning by Martin Görner

• Deep Neural Networks with Box Convolutions, Burkov, E., & Lempitsky, V. (2018): https://lnkd.in/euyMg6r

• Reinforcement Learning: An Introduction Book by Andrew Barto and Richard S. Sutton: https://lnkd.in/ewb_MeJ

• Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov Model by Massimo Caccia and Bruno Rémillard

• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang • Xu • Liang

• Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling by Yaxiong Zeng • Diego Klabjan

• An MIT Press book. By Ian Goodfellow and Yoshua Bengio and Aaron Courville***: https://lnkd.in/ekrAfY3

• Neural networks for stock price prediction, by Yue-Gang Song • Yu-Long Zhou • Ren-Jie Han

• optimization algorithms By Fabian Pedregosa: https://lnkd.in/d9cXkVZ

• The Matrix Calculus You Need For Deep Learning By Terence Parr and Jeremy Howard : https://lnkd.in/dC5MqZM

• Tree Based Modeling from Scratch (in R & Python)

• Jupyter Lab: Evolution of the Jupyter Notebook