CS230: Deep Learning
A distilled compilation of my notes for Stanford's CS230: Deep Learning.
Notes
Deep Learning Intro and Applications
intro; examples of deep learning projects
Deep Learning Intuition
neural network basics
Adversarial Examples and GANs
attacking networks with adversarial/fooling examples; GANs
Full-cycle of a Deep Learning Project
practical aspects of deep learning; optimization
Deep Reinforcement Learning
Markov decision process; Bellman equation; Deep Q-learning
Course Info
Course description:
- Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects.
- You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
Credits
The in-line diagrams are taken from the CS230 lecture slides, unless specified otherwise. Reproduced with permission.
Citation
If you found our work useful, please cite it as:
@misc{Chadha2020DistilledNotesCS230,
author = {Chadha, Aman},
title = {Distilled Notes for Stanford CS230: Deep Learning},
howpublished = {\url{https://www.aman.ai}},
year = {2020},
note = {Accessed: 2020-09-01},
url = {www.aman.ai}
}
A. Chadha, Distilled Notes for Stanford CS230: Deep Learning, https://www.aman.ai, 2020, Accessed: Sept 1 2020.