TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem
Authors: Ankit Jain – Armando Fandango – Amita Kapoor
ISBN-10: 1789132215
ISBN-13: 9781789132212
Publication Date 出版日期: 2018-11-30
Print Length 页数: 322 pages
Book Description
By finelybook
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.
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
