Deep Learning: Fundamentals

Related lectures (29)

Page 2 of 3

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Covers the fundamentals of multilayer neural networks and deep learning.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.