Building Machine Learning Projects with TensorFlow

Key Features
Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production.
This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow
It is a practical and methodically explained guide that allows you to apply Tensorflow’s features from the very beginning.

Book Description
This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production.

What you will learn
Load, interact, dissect, process, and save complex datasets
Solve classification and regression problems using state of the art techniques
Predict the outcome of a simple time series using Linear Regression modeling
Use a Logistic Regression scheme to predict the future result of a time series
Classify images using deep neural network schemes
Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
Resolve character recognition problems using the Recurrent Neural Network (RNN) model
About the Author
Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.

He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008,writing a CPU and GPU - supporting neural network feed forward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.

Table of Contents
Exploring and Transforming Data
Clustering
Linear Regression
Logistic Regression
Simple FeedForward Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks and LSTM
Deep Neural Networks
Running Models at Scale – GPU and Serving
Library Installation and Additional Tips