<aside> 💡 By Vaibhav Bhandari (HomePage, Twitter)

</aside>

From October 17, 2022 - Oreilly Training with Thomas Nield

Slides from the class are - https://drive.google.com/file/d/1AxKuAwQnSseBVJvyElWah8vXV7QThrHu/view?usp=sharing

Github Code Link - https://github.com/thomasnield/oreilly_machine_learning_from_scratch

Notes to self

• Read more of Andrej’s work

References

• Essence of Linear Algebra - https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab by 3Blue1Brown: Covers
• Essential Math for Data Science - https://learning.oreilly.com/library/view/essential-math-for/9781098102920/ by Thomas Nield : Covers Probability, Inferential Statistics, Linear Regression, Neural Networks
• Hands-On Machine Learning - https://learning.oreilly.com/library/view/hands-on-machine-learning/9781098125967/ by Aurélien Géron: Covers End-to-End-Project, Classification, Training Models, Decision Trees, Ensemble Learning and Random Forests, Deep Neural Networks, RNNs, Reinforcement Learning, Special Data Structures, Autodiff, Graphs
• Data Science from Scratch - https://learning.oreilly.com/library/view/data-science-from/9781492041122/ by Joel Grus: Covers Crash Course in Python, Visualizing Data, Linear Algebra, Statistics, Probablity, Hypothesis, Gradience Descent, etal.
• RNNs - http://karpathy.github.io/2015/05/21/rnn-effectiveness/ by Andrej Karpathy (Senior Director of AI at Tesla has PhD in RNNs)