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Applied Deep Learning with TensorFlow 2 Umberto Michelucci

Applied Deep Learning with TensorFlow 2 By Umberto Michelucci

Applied Deep Learning with TensorFlow 2 by Umberto Michelucci

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Applied Deep Learning with TensorFlow 2 Summary

Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python by Umberto Michelucci

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.

This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.

All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.

You will:

* Understand the fundamental concepts of how neural networks work

* Learn the fundamental ideas behind autoencoders and generative adversarial networks

* Be able to try all the examples with complete code examples that you can expand for your own projects

* Have available a complete online companion book with examples and tutorials.


This book is for:

Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.

About Umberto Michelucci

Umberto Michelucci is the founder and the chief AI scientist of TOELT - Advanced AI LAB LLC. He's an expert in numerical simulation, statistics, data science, and machine learning. He has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His first book, Applied Deep Learning-A Case-Based Approach to Understanding Deep Neural Networks, was published in 2018. His second book, Convolutional and Recurrent Neural Networks Theory and Applications was published in 2019. He publishes his research regularly and gives lectures on machine learning and statistics at various universities. He holds a PhD in machine learning, and he is also a Google Developer Expert in Machine Learning based in Switzerland.

Table of Contents

Chapter 1 : Optimization and neural networks
Subtopics: How to read the book Introduction to the book
Chapter 2: Hands-on with One Single NeuronSubtopics: Overview of optimization A definition of learning Constrained vs. unconstrained optimization Absolute and local minima Optimization algorithms with focus on Gradient Descent Variations of Gradient Descent (mini-batch and stochastic) How to choose the right mini-batch size
Chapter 3: Feed Forward Neural NetworksSubtopics: A short introduction to matrix algebra Activation functions (identity, sigmoid, tanh, swish, etc.) Implementation of one neuron in Keras Linear regression with one neuron Logistic regression with one neuron
Chapter 4: RegularizationSubtopics: Matrix formalism Softmax activation function Overfitting and bias-variance discussion How to implement a fully conneted network with Keras Multi-class classification with the Zalando dataset in Keras Gradient descent variation in practice with a real dataset Weight initialization How to compare the complexity of neural networks How to estimate memory used by neural networks in Keras
Chapter 5: Advanced OptimizersSubtopics: An introduction to regularization l_p norm l_2 regularization Weight decay when using regularization Dropout Early Stopping

Chapter 6Chapter Title: Hyper-Parameter tuningSubtopics: Exponentially weighted averages Momentum RMSProp Adam Comparison of optimizers
Chapter 7Chapter Title: Convolutional Neural NetworksSubtopics: Introduction to Hyper-parameter tuning Black box optimization Grid Search Random Search Coarse to fine optimization Sampling on logarithmic scale Bayesian optimisation
Chapter 8Chapter Title: Brief Introduction to Recurrent Neural NetworksSubtopics: Theory of convolution Pooling and padding Building blocks of a CNN Implementation of a CNN with Keras Introduction to recurrent neural networks Implementation of a RNN with Keras

Chapter 9: AutoencodersSubtopics: Feed Forward Autoencoders Loss function in autoencoders Reconstruction error Application of autoencoders: dimensionality reduction Application of autoencoders: Classification with latent features Curse of dimensionality Denoising autoencoders Autoencoders with CNN
Chapter 10: Metric AnalysisSubtopics: Human level performance and Bayes error Bias Metric analysis diagram Training set overfitting How to split your dataset Unbalanced dataset: what can happen K-fold cross validation Manual metric analysis: an example
Chapter 11 Chapter Title: General Adversarial Networks (GANs)Subtopics: Introduction to GANs The building blocks of GANs An example of implementation of GANs in Keras
APPENDIX 1: Introduction to KerasSubtopics: Sequential model Keras Layers Functional APIs Specifying loss functions Putting all together and training a model Callback functions Save and load models
APPENDIX 2: Customizing KerasSubtopics: Custom callback functions Custom training loops Custom loss functions
APPENDIX 3: Symbols and Abbreviations

Additional information

NGR9781484280195
9781484280195
1484280199
Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python by Umberto Michelucci
Umberto Michelucci
New
Paperback
APress
2022-03-29
380
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

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