My Deep Learning Notebooks Repository

In this repository, you can find the implementations of Deep Reinforcement Learning (DRL) algorithms and also implementations of deep learning models for Computer Vision and Natural Language Processing.

Value-based DRL algorithms:

• DQN
• Double DQN
• Dueling DDQN
• Dueling DDQN with PER

Policy-based DRL algorithms:

• REINFORCE
• VPG

Actor-critic DRL algorithms:

• A2C
• A3C
• DDPG
• GAE
• PPO
• SAC
• TD3

Computer Vision models:

Currently, I have implemented the following networks used in Computer Vision tasks:

• AlexNet
• DenseNet
• Inception
• MobileNet
• ResNeXt
• ResNet
• SE-ResNeXt
• SE-ResNet
• ShuffleNet
• SqueezeNet
• VGG
• Xception
• ZFNet

References:

• Playing Atari with Deep Reinforcement Learning

• Deep Reinforcement Learning with Double Q-learning

• Dueling Network Architectures for Deep Reinforcement Learning

• Prioritized Experience Replay

• Asynchronous Methods for Deep Reinforcement Learning

• Continuous control with deep reinforcement learning

• High-Dimensional Continuous Control Using Generalized Advantage Estimation

• Proximal Policy Optimization Algorithms

• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

• Addressing Function Approximation Error in Actor-Critic Methods

• Densely Connected Convolutional Networks

• Going Deeper with Convolutions

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

• Aggregated Residual Transformations for Deep Neural Networks

• Deep Residual Learning for Image Recognition

• Squeeze-and-Excitation Networks

• ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

• Very Deep Convolutional Networks for Large-Scale Image Recognition

• Xception: Deep Learning with Depthwise Separable Convolutions