Deep Q-Learning: DeepRL1.1

Related lectures (29)

Page 3 of 3

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Explores the optimization of filters in a convolutional layer and the backpropagation process for MaxPooling.

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.