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Discipline Module 3 · Predictive Models

Artificial Neural Networks and Deep Learning

Professor
Sarah
Workload
30h
Syllabus

Concepts and modeling of artificial neurons and artificial neural networks. Perceptron. Multilayer Perceptron: architecture, activation functions, cost functions, training with the backpropagation algorithm, and applications to regression and classification tasks. Deep neural networks: foundations, architecture examples, and modern applications for text and image processing.

Content
  • Concepts and modeling of artificial neurons and artificial neural networks
  • Perceptron
  • Multilayer Perceptron: architecture, activation functions, cost functions, training with the backpropagation algorithm, and applications to regression and classification tasks
  • Deep neural networks: foundations, architecture examples, and modern applications for text and image processing
Core Bibliography
  • Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. Deep Learning. MIT Press, 2016.
  • Aggarwal, C. C. Neural Networks and Deep Learning: A Textbook. 1. ed. Springer International Publishing, EUA, 2018. 497 p. ISBN 978-3-319-94462-3.
  • Géron, Aurélien. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., 3ed. 2022.
  • Braga, A. de C.; Carvalho, A. P. de L. F.; Ludermir, T. B. Redes Neurais - Artificiais: Teoria e Aplicações. 1. ed. Edição Português, 2007.
Complementary Bibliography
  • Krohn, Jon; Beyleveld, Grant; Bassens, Aglaé. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence. Addison-Wesley Professional, 1. ed., 2019.
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