Mathematics of Data: From Theory to Computation

Related lectures (32)

Page 3 of 4

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

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

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

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

Covers the fundamentals of multilayer neural networks and deep learning.

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

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

Covers the basics of neural networks, including the perceptron model and backpropagation.

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

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