By visiting our site, you agree to our privacy policy regarding cookies, tracking statistics, etc.

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

Curriculum Builder

View Curriculum

Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Theodoridis, Sergios, and Konstantinos Koutroumbas. Pattern Recognition. 4th ed. Burlington Heidelberg: Academic Press, 2009.

Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. “Dive into Deep Learning.” arXiv, August 22, 2023.

http://arxiv.org/abs/2106.11342

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

http://neuralnetworksanddeeplearning.com

Tunstall, Lewis, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers: Building Language Applications with Hugging Face. First edition. Sebastopol, CA: O’Reilly Media, 2022.

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

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. Adaptive Computation and Machine Learning Series. The MIT Press, 2016.

Goldberg, Yoav. “A Primer on Neural Network Models for Natural Language Processing.” arXiv, 2015.

https://doi.org/10.48550/ARXIV.1510.00726

Eisenstein, Jacob. Introduction to Natural Language Processing. Adaptive Computation and Machine Learning. The MIT Press, 2019.

Jurafsky, Daniel, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall Series in Artificial Intelligence. Upper Saddle River, N.J: Prentice Hall, 2000.

Thank you for signing up to our newsletter!

Thank you! RSVP received for Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Thank you for applying! We will be in touch.

Apply for: Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Loading...

Loading…