Recommender systems are widely used by web retailers to suggest products to their customers; for example, Amazon tells you what other customers who purchased this item were interested in or Netflix suggests TV serials and movies based on what you have watched and what other Netflix users with the same interest have watched. These recommender systems work on the basis of collaborative filtering. In collaborative filtering, the system builds a model from a user's past behavior. We will use the RBM, made in the previous recipe, to build a recommender system using collaborative filtering to recommend movies. An important challenge in this work is that most users will not rate all products/movies, thus most data is missing. If there are M products and N users, then we need to build an array, N x M, which contains the known ratings of the users and...
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.
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