(From a Pytorch demo) We'll use this Google Colab to implement DQN: https://colab.research.google.com/drive/1z4IBOlA2YR_mCohgUDnqxNIpVDOaPTR9?usp=sharing solution here: https://colab.research.google.com/drive/1JlNPU8uYMatTHQB6PtoBpEpM9u0AWnX1?usp=sharing
From Google, RNN demo: https://colab.research.google.com/github/tensorflow/docs/blob/snapshot-keras/site/en/guide/keras/rnn.ipynb?authuser=1#scrollTo=fb8de09c4343
CNN CIFAR10 exercise: https://colab.research.google.com/drive/1RClhxJh30Qs5rt-sS3BznZvOuWeW5CPB?usp=sharing
After class, I'll post a "solved" version in the Lecture 4 folder.
Lecture notes will be updated periodically.
Helpful resources for reading:
-
"Deep Learning" by Goodfellow, Bengio, Courville
-
"Understanding Deep Learning" by J.D. Prince
-
"Dive into Deep Learning" https://d2l.ai/d2l-en.pdf
Andrew Ng's Coursera lectures on Deep learning are especially insightful: https://www.coursera.org/specializations/deep-learning
And for writing code:
Some other helpful resources:
- https://github.com/Atcold/NYU-DLSP20
- Sandbox demo for training NNs: https://ml-playground.com/#
- http://d2l.ai