Neural Networks Convergence

Related lectures (31)

Page 3 of 4

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

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Covers the perceptron model and backpropagation algorithm in neural networks.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Covers PyTorch tensor data structure and training a CNN to classify images.

Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.