Deep Learning

This module explores deep learning, a subset of machine learning that utilizes neural networks with many layers to model complex patterns in data. It covers foundational concepts like convolutional and recurrent neural networks, backpropagation, and training techniques, as well as applications in image and speech recognition, natural language processing, and autonomous vehicles.

Portal > Artificial Intelligence > Deep Learning

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Jurafsky, Dan and James H. Martin. “Speech and Language Processing”, 3rd Edition, (2024 pre-release)

Eisenstein, Jacob. “Introduction to Natural Language Processing”, The MIT Press, 2019.

Goldberg, Yoav. “A Primer on Neural Network Models for Natural Language Processing,” 2015. doi:10.48550/arxiv.1510.00726.

Goodfellow, Ian, Bengio, Yoshua, and Aaron Courville. “Deep Learning”, The MIT Press, 2016.

Rao, Delip, and Brian McMahan. Natural Language Processing with PyTorch : Build Intelligent Language Applications Using Deep Learning. First edition. Beijing: O’Reilly Media, 2019.

Tunstall, Lewis, von Werra, Leandro, and Thomas Wolf. “Natural Language Processing with Transformers”, O’Reilly Media, 2022.

Nielsen, A Michael, “Neural Networks and Deep Learning”, 2019.

Zhang, Aston, Zachary C Lipton, Mu Li, and Alexander J Smola. “Dive into Deep Learning,” 2023. doi:10.48550/arxiv.2106.11342.

Theodoridis, S. and K. Koutroumbas “Pattern Recognition.” Edition 4, Academic Press, 2008.

Sutton, Richard, and Andrew Barto. “Reinforcement Learning: An Introduction,” 2nd ed. The MIT Press, 2018.