Deep Learning with TensorFlow Paperback - 24 Apr 2017
by Giancarlo Zaccone
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide
About This Book
- Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
- Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
- Real-world contextualization through some deep learning problems concerning research and application
Who This Book Is For
The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.
After finishing the book, you will be familiar with machine learning techniques, in particular, the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python Paperback - December 12, 2017
by Antonio Gulli
Skill up and implement tricky neural networks using Google's TensorFlow 1.x
An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in 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 independent recipes. You will learn how to make Keras as backend with TensorFlow.
Tensorflow Machine Learning Cookbook Paperback - 2017
by Nick McClure
Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook
About This Book
Your quick guide to implementing TensorFlow in your day-to-day machine learning activities
Learn advanced techniques that bring more accuracy and speed to machine learning * Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow
Who This Book Is For
This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.
What You Will Learn
- Become familiar with the basics of the TensorFlow machine learning library
- Get to know Linear Regression techniques with TensorFlow
- Learn SVMs with hands-on recipes
- Implement neural networks and improve predictions
- Apply NLP and sentiment analysis to your data
- Master CNN and RNN through practical recipes
Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python Paperback - 7 Jan 2018
by Alexey Grigorev
- Build efficient deep learning pipelines using the popular Tensorflow framework
- Train neural networks such as ConvNets, generative models, and LSTMs
- Includes projects related to Computer Vision, stock prediction, chatbots and more
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.
TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.
Who this book is for
This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.