Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Explores Stochastic Gradient Descent and Mean Field Analysis in two-layer neural networks, emphasizing their iterative processes and mathematical foundations.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
