Crash course on Deep Learning

Related lectures (32)

Page 3 of 4

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Covers the fundamentals of multilayer neural networks and deep learning.

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

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

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

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

Explores data representations, histograms, neural networks, and deep learning concepts.