Learning Tensorflow ─ A Guide to Building Deep Learning Systems

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
• Deploy TensorFlow on GPU clusters, and serve output in a production setting

Tom Hope is an applied researcher in Computer Science, and a PhD student at the Hebrew University, working on Machine Learning, Deep Learning, Natural Language Processing (NLP) and network data. He has worked for Intel Corp. for the past 4 years, leading data science and Deep Learning R&D across multiple projects and domains including in web mining, text analytics, sales and marketing, IoT (machine-generated data), financial forecasting, large-scale manufacturing. He is co-founder of two stealth start-ups in analytics / Deep Learning. Previously he was at Tapingo (an e-commerce start-up) in its early stages, doing end-to-end data science R&D in cold-start conditions. In the past, Tom dabbled in journalism, writing for an English-language international newspaper.

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. Hezi is a co-founder of a start-up focusing on Deep Learning.

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 Intel Corp., leading Deep Learning R&D in sales and marketing. Itay is a co-founder of a start-up doing analytics with Deep Learning, and a lecturer teaching engineering students.