BackProp Algorithm: Pseudocode and Processing Steps

Related lectures (29)

Page 2 of 3

Covers the fundamental building blocks of deep learning, including tensors, backpropagation, and PyTorch.

Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.

Discusses automatic differentiation, emphasizing reverse mode differentiation for optimizing convolutional layer filters by gradient descent.

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.

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

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

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

Covers the backpropagation algorithm for training neural networks and the representation of functions in multilayer networks.

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.