Lu, Yukuan

Deep Q-learning or deep Q-network (DQN) is one of the earliest and most successful algorithms that introduce deep neural networks into RL.

DQN is not Q learning, at least not the specific Q learning algorithm introduced in my post, but it shares the core idea of Q learning.

Sources:

1. Shiyu Zhao. Chapter 8: Value Function Approximation. Mathematical Foundations of Reinforcement Learning.
2. DQN 2013 paper
3. Reinforcement Learning Explained Visually (Part 5): Deep Q Networks, step-by-step by Ketan Doshi
4. My github repo for DQN

Sources:

1. SimCLR v1 2020 paper
2. SimCLR v2 2020 paper
3. Contrastive Representation Learning by Lilian
4. UVA's SimCLR implementation(Both Pytorch and Jax versions are implemented)

Sources:

1. CUDA Toolkit 12.4 Update 1 Downloads
2. cuDNN Downloads

Sources:

1.PyTorch document

Sources:

1. B. P. Lathi & Roger Green. (2021). Chapter 1: Signals and Systems. Signal Processing and Linear Systems (2nd ed., pp. 64-136). Oxford University Press.

Sources:

1. B. P. Lathi & Roger Green. (2021). Chapter 1: Signals and Systems. Signal Processing and Linear Systems (2nd ed., pp. 64-136). Oxford University Press.
2. James McClellan, Ronald Schafer & Mark Yoder. (2015). Sinusoids. DSP First (2nd ed., pp. 9-40). Pearson.

Sources:

1. B. P. Lathi & Roger Green. (2021). Chapter 1: Signals and Systems. Signal Processing and Linear Systems (2nd ed., pp. 64-136). Oxford University Press.

Sources:

1. VQ-VAE 2018 paper
2. A compact explanation by Julius Ruseckas

Link: My VQ-VAE implementation on Github

Sources:

1. JAX Quickstart
2. Learning JAX as a PyTorch developer

In Python, a metaclass creates and defines the behavior of other classes, since classes themselves are objects.

Sources:

1. Demystifying Python Metaclasses: Understanding and Harnessing the Power of Custom Class Creation
2. Python Metaclasses by John Sturtz.