This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.
An attempt to create a convenient workspace that makes it possible to work with multiple custom python libraries, while keeping all benefits of Google Colaboratory.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
One of the hardest concepts to grasp when learning about Convolutional Neural Networks for object detection is the idea of anchor boxes. It is also one of the most important parameters you can tune…
In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building…