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TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers.
With this book you will learn how to:
Get up and running with TensorFlow, rapidly and painlessly
Build and train popular deep learning models for computer vision and NLP
Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs
Train models at scale, and deploy TensorFlow in a production setting
Chapter 2Go with the Flow: Up and running with TensorFlow
Chapter 3Understanding TensorFlow Basics
Chapter 4Convolutional Neural Networks
Chapter 5Text I: Working with text and sequences + TensorBoard visualization
Chapter 6Text II: Word vectors, advanced RNN and embedding visualization
Chapter 7TensorFlow abstractions and simplifications
Chapter 8Queues, threads, and reading data
Chapter 9Distributed TensorFlow
Chapter 10Exporting and serving models with TensorFlow
Tom Hope is an applied machine learning researcher and data scientist with extensive background in academia and industry.
He has background as a senior data scientist in large international corporation settings, leading data science and deep learning R&D across multiple domains including web mining, text analytics, computer vision,sales and marketing, IoT, financial forecasting and large-scale manufacturing. Previously he was at a successful e-commerce startup in its early days, leading data science R&D. He has also served as a data science consultant for major international companies and startups. His research in computer science, data mining and statistics revolves around machine learning, deep learning, NLP, weak supervision and time-series.
Hezi Reshef is an applied researcher and PhD student in Machine Learning at the Hebrew University, developing Machine Learning and Deep Learning methods for wearable device data, and working on using wearable devices to monitor patient health. He has worked at Intel Corp., leading Deep Learning R&D for monitoring and predicting patient outcomes using remote sensing and wearables. Prior to Intel, Hezi was at Microsoft, leading Machine Learning R&D for mining telemetry data, predicting software bugs, user segmentation, and other projects.
Itay Lieder is an applied researcher in Machine Learning and Computational Neuroscience and a PhD student at the Hebrew University, in collaboration with the Gatsby Computational Neuroscience Unit at UCL, studying the human perception with massive crowd-sourcing experiments on Amazon Turk. His current work focuses on predicting and understanding the way humans react to sounds (e.g. music), via multiple online interactive experiments. He has worked for large international corporations, leading Deep Learning R&D in text analytics and web mining for sales and marketing.