Language Models: Fixed-context and Recurrent Neural Networks

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Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Covers the development of transformers and their impact on attention mechanisms in NLP.

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

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

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

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

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

Covers the training of a binary sentiment classifier using an RNN.