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TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

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Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines.

TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms.

  • Learn TensorFlow fundamentals, including how to perform basic computation
  • Build simple learning systems to understand their mathematical foundations
  • Dive into fully connected deep networks used in thousands of applications
  • Turn prototypes into high-quality models with hyperparameter optimization
  • Process images with convolutional neural networks
  • Handle natural language datasets with recurrent neural networks
  • Use reinforcement learning to solve games such as tic-tac-toe
  • Train deep networks with hardware including GPUs and tensor processing units
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Algorithms in a Nutshell: A Practical Guide
Algorithms in a Nutshell: A Practical Guide

Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your...

Machine Learning and Security: Protecting Systems with Data and Algorithms
Machine Learning and Security: Protecting Systems with Data and Algorithms

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security...


Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...

Blockchain: A Practical Guide to Developing Business, Law, and Technology Solutions
Blockchain: A Practical Guide to Developing Business, Law, and Technology Solutions
Develop, validate, and deploy powerful decentralized applications using blockchain 


Get the most out of cutting-edge blockchain technology using the hands-on information contained in this comprehensive resource. Written by a team of technology and legal experts, Blockchain:...
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

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