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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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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...

Big Data Visualization
Big Data Visualization
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book...
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

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Cloud Computing and Virtualization
Cloud Computing and Virtualization

The purpose of this book is first to study cloud computing concepts, security concern in clouds and data centers, live migration and its importance for cloud computing, the role of firewalls in domains with particular focus on virtual machine (VM) migration and its security concerns. The book then tackles design, implementation of...

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics
Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics

Key Features

  • This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way
  • Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
  • Get simple examples and real-life data...
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