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Deep Learning for Beginners

You're reading from  Deep Learning for Beginners

Product type Book
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Pages 432 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Dr. Pablo Rivas Dr. Pablo Rivas
Profile icon Dr. Pablo Rivas
LinkedIn
Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.
See other products by Dr. Pablo Rivas

Table of Contents (20) Chapters

Preface Section 1: Getting Up to Speed
Introduction to Machine Learning Setup and Introduction to Deep Learning Frameworks Preparing Data Learning from Data Training a Single Neuron Training Multiple Layers of Neurons Section 2: Unsupervised Deep Learning
Autoencoders Deep Autoencoders Variational Autoencoders Restricted Boltzmann Machines Section 3: Supervised Deep Learning
Deep and Wide Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Final Remarks on the Future of Deep Learning Other Books You May Enjoy

References

  • Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
  • Muselli, M. (1997). On convergence properties of the pocket algorithm. IEEE Transactions on Neural Networks, 8(3), 623-629.
  • Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., & Schoenholz, S. S. (2019). Neural Tangents: Fast and Easy Infinite Neural Networks in Python. arXiv preprint arXiv:1912.02803.
  • Soltanolkotabi, M., Javanmard, A., & Lee, J. D. (2018). Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. IEEE Transactions on Information Theory, 65(2), 742-769.
  • Du, S. S., Zhai, X., Poczos, B., & Singh, A. (2018). Gradient descent provably optimizes over-parameterized neural networks. arXiv preprint arXiv:1810.02054.
  • Liao, Q., Miranda, B., Banburski, A., Hidary, J., & Poggio, T. (2018). A surprising linear relationship predicts test performance...
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Authors (1)

Profile icon Dr. Pablo Rivas
LinkedIn
Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.
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See other products by Dr. Pablo Rivas

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