TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem

by Ankit Jain, Armando Fandango, Amita Kapoor

Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects 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. 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

510 pages, Kindle Edition

Published November 30, 2018

Reviews

[Maria-Anna · Rating 3 out of 5]

-It's hard to define what level of knowledge this book requires. It tries to explain everything in very simple terms for each of the project described in the book like what is Reinforcement Learning and how LSTM networks work. But then it quickly jumps to the solution of the problem and if you're a beginner it would be hard to understand what is used and why. It's like you can only grasp some very shallow explanations of deep learning concepts and understanding of potential usage of them but that's it. For an experienced reader, this book is definitely not enough - you would like to dive deeper but it feels you have to just read what neural network is and then look at the architecture of some RNN model and just accept it. For me, this book serves as a compilation of potential use cases for deep learning but that's it.