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 Tecnologica 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.

Table of Contents

Chapter 1. Exploring and Transforming Data
Chapter 2. Clustering
Chapter 3. Linear Regression
Chapter 4. Logistic Regression
Chapter 5. Simple FeedForward Neural Networks
Chapter 6. Convolutional Neural Networks
Chapter 7. Recurrent Neural Networks and LSTM
Chapter 8. Deep Neural Networks
Chapter 9. Running Models at Scale – GPU and Serving
Chapter 10. Library Installation and Additional Tips