Tackle common commercial machine learning problems with Google's TensorFlow 1.x library and build deployable solutions.
About This Book
- Enter the new era of second-generation machine learning with Python with this practical and insightful guide
- Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support
- Create pipelines for training and using applying classifiers using raw real-world data
Who This Book Is For
This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected.
What You Will Learn
- Explore how to use different machine learning models to ask different questions of your data
- Learn how to build deep neural networks using TensorFlow 1.x
- Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x
- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
- Discover how to embed your machine learning model in a web application for increased accessibility
- Learn how to use multiple GPUs for faster training using AWS
In Detail
Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x.
Firstly, you'll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You'll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you'll implement a complete real-life production system from training to serving a deep learning model. As you advance you'll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you'll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.
By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Style and approach
This comprehensive guide will enable you to understand the latest advances in machine learning and will empower you to implement this knowledge in your machine learning environment.
