Applied Deep Learning A Case-Based Approach to Understanding Deep Neural Networks

Applied Deep Learning A Case-Based Approach to Understanding Deep Neural Networks / by Umberto Michelucci.

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single ne...

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Bibliographic Details
Main Author: Michelucci, Umberto (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berkeley, CA : Apress : Imprint: Apress, 2018.
Edition:1st ed. 2018.
Series:Springer eBook Collection.
Subjects:
Artificial intelligence.
Python (Computer program language).
Open source software.
Computer programming.
Big data.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Chapter 1: Introduction
  • Chapter 2: Single Neurons
  • Chapter 3: Fully connected Neural Network with more neurons
  • Chapter 4: Neural networks error analysis
  • Chapter 5: Dropout technique
  • Chapter 6: Hyper parameters tuning
  • Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.)
  • Chapter 8: Convolutional Networks and image recognition
  • Chapter 9: Recurrent Neural Networks
  • Chapter 10: A practical COMPLETE example from scratch (put everything together)
  • Chapter 11: Logistic regression implement from scratch in Python without libraries. .

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