TensorFlow Machine Learning Projects

TensorFlow Machine Learning Projects


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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 Description

TensorFlow 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 for

TensorFlow 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

Product Details

ISBN-13: 9781789132212
Publisher: Packt Publishing
Publication date: 11/30/2018
Pages: 322
Product dimensions: 7.50(w) x 9.25(h) x 0.67(d)

About the Author

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.

Table of Contents

Table of Contents

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

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