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Название: Deep Learning Quick Reference: Over 10 secret hacks for training and optimizing deep neural networks with TensorFlow and Keras
Автор: Mike Bernico
Издательство: Packt Publishing
ISBN: 1788837991
Год: 2018
Страниц: 272
Язык: английский
Формат: epub +code
Размер: 23.8 MB

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide.

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples.

You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks.

By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.

What You Will Learn:

Solve regression and classification challenges with TensorFlow and Keras
Learn to use Tensor Board for monitoring neural networks and its training
Optimize hyperparameters and safe choices/best practices
Build CNN's, RNN's, and LSTM's and using word embedding from scratch
Build and train seq2seq models for machine translation and chat applications.
Understanding Deep Q networks and how to use one to solve an autonomous agent problem.
Explore Deep Q Network and address autonomous agent challenges.

Table of Contents:

1: THE BUILDING BLOCKS OF DEEP LEARNING
2: USING DEEP LEARNING TO SOLVE REGRESSION PROBLEMS
3: MONITORING NETWORK TRAINING USING TENSORBOARD
4: USING DEEP LEARNING TO SOLVE BINARY CLASSIFICATION PROBLEMS
5: USING KERAS TO SOLVE MULTICLASS CLASSIFICATION PROBLEMS
6: HYPERPARAMETER OPTIMIZATION
7: TRAINING A CNN FROM SCRATCH
8: TRANSFER LEARNING WITH PRETRAINED CNNS
9: TRAINING AN RNN FROM SCRATCH
10: TRAINING LSTMS WITH WORD EMBEDDINGS FROM SCRATCH
11: TRAINING SEQ2SEQ MODELS
12: USING DEEP REINFORCEMENT LEARNING
13: GENERATIVE ADVERSARIAL NETWORKS

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