Unlike in other examples in this book, here we will need to cover both the target domain problem, the source domain problem, and the network architecture we're using. We will start with an overview of the target domain, which is the problem we're trying to solve. Then we will cover the source domain our network was originally trained on and briefly cover the network architecture we will be using. Then, we will spend the rest of the chapter wiring the problem together. We need to consider both domains separately because their size and similarity are closely related to network performance. The closer the target and source are in type, the better the results.
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Deep Learning Quick Reference
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Deep Learning Quick Reference
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Overview of this book
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.
Table of Contents (15 chapters)
Preface
Free Chapter
The Building Blocks of Deep Learning
Using Deep Learning to Solve Regression Problems
Monitoring Network Training Using TensorBoard
Using Deep Learning to Solve Binary Classification Problems
Using Keras to Solve Multiclass Classification Problems
Hyperparameter Optimization
Training a CNN from Scratch
Transfer Learning with Pretrained CNNs
Training an RNN from scratch
Training LSTMs with Word Embeddings from Scratch
Training Seq2Seq Models
Using Deep Reinforcement Learning
Generative Adversarial Networks
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