Overview

Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key Features Use machine learning and deep learning principles to build real-world projects Get to grips with TensorFlow's impressive range of module offerings Implement projects on GANs, reinforcement learning, and capsule network Book DescriptionTensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learn Understand the TensorFlow ecosystem using various datasets and techniques Create recommendation systems for quality product recommendations Build projects using CNNs, NLP, and Bayesian neural networks Play Pac-Man using deep reinforcement learning Deploy scalable TensorFlow-based machine learning systems Generate your own book script using RNNs Who this book is forTensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques

Full Product Details

Author:   Ankit Jain ,  Armando Fandango ,  Amita Kapoor
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781789132212

ISBN 10:   1789132215
Pages:   322
Publication Date:   30 November 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

Table of Contents Overview of Tensorflow and Machine Learning Using Machine Learning to detect exoplanets in outer space Sentiment Analysis in your browser using Tensorflow.js Digit Classification using Tensorflow Lite Speech to text and topic extraction using NLP Predicting Stock Prices using Gaussian Process Regression Credit Card Fraud Detection using Autoencoders Generating Uncertainty in Traffic Signs Classifier using Bayesian Neural Networks Generating Matching Shoe Bags from Shoe Images Using DiscoGANs Classifying Clothing Images using Capsule Networks Making Quality Product Recommendations Using TensorFlow Object detection at a large scale with Tensorflow Generating Book Scripts Using LSTMs Playing Pacman using Deep Reinforcement Learning What is next?

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Author Information

Ankit Jain currently works as a senior research scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber's problems, ranging from forecasting and food delivery to self-driving cars. Previously, he has worked in a variety of data science roles at the Bank of America, Facebook, and other start-ups. He has been a featured speaker at many of the top AI conferences and universities, including UC Berkeley, O'Reilly AI conference, and others. He has a keen interest in teaching and has mentored over 500 students in AI through various start-ups and bootcamps. He completed his MS at UC Berkeley and his BS at IIT Bombay (India). Armando Fandango creates AI empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as Chief Data Scientist and Director at startups and large enterprises. He has been advising high-tech AI-based startups. Armando has authored books titled Python Data Analysis - Second Edition and Mastering TensorFlow. He has also published research in international journals and conferences. Amita Kapoor is an Associate Professor at the Department of Electronics, SRCASW, University of Delhi. She has been teaching neural networks for twenty years. During her PhD, she was awarded the prestigious DAAD fellowship, which enabled her to pursue part of her research work at the Karlsruhe Institute of Technology, Germany. She was awarded the Best Presentation Award at the International Conference on Photonics 2008. Being a member of the ACM, IEEE, INNS, and ISBS, she has published more than 40 papers in international journals and conferences. Her research areas include machine learning, AI, neural networks, robotics, and Buddhism and ethics in AI. She has co-authored the book, Tensorflow 1.x Deep Learning Cookbook, by Packt Publishing.

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