Files

01. Fundamentals of Reinforcement Learning
02. A Guide to the Gym Toolkit
03. Bellman Equation and Dynamic Programming
04. Monte Carlo Methods
05. Understanding Temporal Difference Learning
06. Case Study: The MAB Problem
07. Deep learning foundations
- .ipynb_checkpoints
- images
- 7.05 Building Neural Network from scratch.ipynb
- README.md
08. A primer on TensorFlow
09. Deep Q Network and its Variants
10. Policy Gradient Method
11. Actor Critic Methods - A2C and A3C
12. Learning DDPG, TD3 and SAC
13. TRPO, PPO and ACKTR Methods
14. Distributional Reinforcement Learning
15. Imitation Learning and Inverse RL
16. Deep Reinforcement Learning with Stable Baselines
17. Reinforcement Learning Frontiers
images
pdf
.DS_Store
README.md

07. Deep learning foundations

Oct 2, 2020

ce93744 · Oct 2, 2020

Name	Name	Last commit message	Last commit date
parent directory ..
.ipynb_checkpoints	.ipynb_checkpoints	update	Oct 2, 2020
images	images	update	Oct 2, 2020
7.05 Building Neural Network from scratch.ipynb	7.05 Building Neural Network from scratch.ipynb	update	Oct 2, 2020
README.md	README.md	update	Oct 2, 2020

7.1. Biological and artifical neurons
7.2. ANN and its layers
7.3. Exploring activation functions
7.4. Forward and backward propagation in ANN
- 7.4.1. Forward propgation in ANN
- 7.4.2. Backward propagation in ANN
7.5. Building neural network from scratch
7.6. Recurrent neural networks
- 7.6.1. Difference between feedforward networks and RNN
- 7.6.2. Forward propagation in RNN
- 7.6.3. Backpropagation through time
7.7. LSTM-RNN
- 7.7.1. Understanding the LSTM cells
7.8. Convolutional neural networks
- 7.8.1. Understanding every layers in CNN
- 7.8.2. Architecture of CNN
7.9. Generative adversarial networks
- 7.9.1. Understanding generator
- 7.9.2. Understanding discriminator
- 7.9.3. Architecture of GAN
- 9.9.4. Understanding the loss function