Applied Deep Learning A Case Based Approach to Understanding Deep Neural Networks 1st edition by Umberto Michelucci ISBN B07H6D9NQ8 978-1484237908

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ISBN 10: B07H6D9NQ8
ISBN 13: 978-1484237908
Author: 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 neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. 

The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. 

Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). 

Applied Deep Learning A Case Based Approach to Understanding Deep Neural Networks 1st Table of contents:

1. Computational Graphs and TensorFlow

2. Single Neuron

3. Feedforward Neural Networks

4. Training Neural Networks

5. Regularization

6. Metric Analysis

7. Hyperparameter Tuning

8. Convolutional and Recurrent Neural Networks

9. A Research Project

10. Logistic Regression from Scratch

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Tags: Umberto Michelucci, Applied Deep, Case Based, Understanding Deep