Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin

Key FeaturesBored 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 TensorFlowIt is a practical and methodically explained guide that allows you to apply Tensorflows features from the very beginning.Book DescriptionThis 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 learnLoad, interact, dissect, process, and save complex datasetsSolve classification and regression problems using state of the art techniques Predict the outcome of a simple time series using Linear Regression modelingUse a Logistic Regression scheme to predict the future result of a time seriesClassify images using deep neural network schemesTag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layerResolve character recognition problems using the Recurrent Neural Network (RNN) modelAbout the AuthorRodolfo 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. More recently hes been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.Table of ContentsExploring and Transforming DataClusteringLinear RegressionLogistic RegressionSimple FeedForward Neural NetworksConvolutional Neural NetworksRecurrent Neural Networks and LSTMDeep Neural NetworksRunning Models at Scale – GPU and ServingLibrary Installation and Additional Tips