Autoencoders covered so far (except for CAEs) consisted only of a single-layer encoder and a single-layer decoder. However, it is possible for us to have multiple layers in encoder and decoder networks; using deeper encoder and decoder networks can allow the autoencoder to represent complex features. The structure so obtained is called a Stacked Autoencoder (Deep Autoencoders); the features extracted by one encoder are passed on to the next encoder as input. The stacked autoencoder can be trained as a whole network with an aim to minimize the reconstruction error, or each individual encoder/decoder network can be first pretrained using the unsupervised method you learned earlier, and then the complete network is fine-tuned. It has been pointed out that, by pretraining, also called Greedy layer-wise training, the results are better.
TensorFlow 1.x Deep Learning Cookbook
TensorFlow 1.x Deep Learning Cookbook
Overview of this book
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.
In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on
some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.
By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,
GANs, and autoencoders.
Related Content you might be interested in
Current Title:
TensorFlow 1.x Deep Learning Cookbook
Table of Contents (15 chapters)
Preface
Free Chapter
TensorFlow - An Introduction
Regression
Neural Networks - Perceptron
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Recurrent Neural Networks
Unsupervised Learning
Autoencoders
Reinforcement Learning
Mobile Computation
Generative Models and CapsNet
Distributed TensorFlow and Cloud Deep Learning
Learning to Learn with AutoML (Meta-Learning)
TensorFlow Processing Units
Customer Reviews