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Learn how to solve challenging machine learning problems with Tensorflow, Google’s revolutionary new system for deep learning. If you have some background in basic linear algebra and calculus, this practical book shows you how to build—and when to use—deep learning architectures. You’ll learn how to design systems capable of detecting objects in images, understanding human speech, analyzing video, and predicting the properties of potential medicines.

TensorFlow for Deep Learning teaches concepts through practical examples and builds an understanding of deep learning foundations from the ground up. It’s ideal for practicing developers comfortable with designing software systems, but not necessarily with creating learning systems. This book is also useful for scientists and other professionals who are comfortable with scripting but not necessarily with designing learning algorithms.

• Gain in-depth knowledge of the TensorFlow API and primitives.
• Understand how to train and tune machine learning systems with TensorFlow on large datasets.
• Learn how to use TensorFlow with convolutional networks, recurrent networks, LSTMs, and reinforcement learning.

• 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

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning each using Google’s machine learning library TensorFlow.

This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

• 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
• Take TensorFlow into production

Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar’s Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John’s University.

He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his Twitter account, @nfmcclure.

1. Getting Started with TensorFlow
2. The TensorFlow Way
3. Linear Regression
4. Support Vector Machines
5. Nearest Neighbor Methods
6. Neural Networks
7. Natural Language Processing
8. Convolutional Neural Networks
9. Recurrent Neural Networks
10. Taking TensorFlow to Production
11. More with TensorFlow

Naturally, you are excited about Machine Learning and you would love to join the party!

Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look.

For example:

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.

This practical book shows you how by using concrete examples, minimal theory and two production-ready Python frameworks Scikit-learn and TensorFlow author Aurelien Geron help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural netsUse Scikit-learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details.

Aurélien Geron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib’.

Before this, he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: He taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn’t open on the 2nd jump.

1. The Machine Learning Landscape
2. End-to-End Machine Learning Project
3. Classification
4. Training Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction
9. Up and Running with Tensorflow
10. Introduction to Artificial Neural Networks
11. Training Deep Neural Nets
12. Distributing Tensorflow Across Devices and Servers
13. Convolutional Neural Networks
14. Recurrent Neural Networks
15. Autoencoders
16. Reinforcement Learning