Recurrent Neural Networks: Training and Challenges

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Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Deep Learning: Data Representations and Neural Networks

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

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Multilayer Perceptron: Training and Optimization

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

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Neural Networks: Regression and Classification

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

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Deep Learning Building Blocks

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

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.