Building Machine Learning Projects with TensorFlow

Engaging projects that will teach you how complex data can be exploited to gain the most insight
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Building Machine Learning Projects with TensorFlow

Rodolfo Bonnin

4 customer reviews
Engaging projects that will teach you how complex data can be exploited to gain the most insight
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Book Details

ISBN 139781786466587
Paperback282 pages

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.

Table of Contents

Chapter 1: Exploring and Transforming Data
TensorFlow's main data structure - tensors
Handling the computing workflow - TensorFlow's data flow graph
Running our programs - Sessions
Basic tensor methods
Summary
Chapter 2: Clustering
Learning from data - unsupervised learning
Clustering
k-means
k-nearest neighbors
Project 1 - k-means clustering on synthetic datasets
Project 2 - nearest neighbor on synthetic datasets
Summary
Chapter 3: Linear Regression
Univariate linear modelling function
Determination of the cost function
Minimizing the cost function
Example section
Example 1 - univariate linear regression
Example - multivariate linear regression
Summary
Chapter 4: Logistic Regression
Problem description
Logistic function predecessor - the logit functions
The logistic function
Example 1 - univariate logistic regression
Example 2 - Univariate logistic regression with skflow
Summary
Chapter 5: Simple FeedForward Neural Networks
Preliminary concepts
First project - Non linear synthetic function regression
Second project - Modeling cars fuel efficiency with non linear regression
Third project - Learning to classify wines: Multiclass classification
Summary
Chapter 6: Convolutional Neural Networks
Origin of convolutional neural networks
Example 1 - MNIST digit classification
Example 2 - image classification with the CIFAR10 dataset
Summary
Chapter 7: Recurrent Neural Networks and LSTM
Recurrent neural networks
Example 1 - univariate time series prediction with energy consumption data
Example 2 - writing music "a la" Bach
Summary
Chapter 8: Deep Neural Networks
Deep neural network definition
Deep network architectures through time
Alexnet
Inception v3
Residual Networks (ResNet)
Example - painting with style - VGG style transfer
Summary
Chapter 9: Running Models at Scale – GPU and Serving
GPU support on TensorFlow
Example 1 - assigning an operation to the GPU
Example 2 - calculating Pi number in parallel
Distributed TensorFlow
Example 3 - distributed Pi calculation
Example 4 - running a distributed model in a cluster
Summary
Chapter 10: Library Installation and Additional Tips
Linux installation
Windows installation
MacOS X installation
Summary

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

Authors

Table of Contents

Chapter 1: Exploring and Transforming Data
TensorFlow's main data structure - tensors
Handling the computing workflow - TensorFlow's data flow graph
Running our programs - Sessions
Basic tensor methods
Summary
Chapter 2: Clustering
Learning from data - unsupervised learning
Clustering
k-means
k-nearest neighbors
Project 1 - k-means clustering on synthetic datasets
Project 2 - nearest neighbor on synthetic datasets
Summary
Chapter 3: Linear Regression
Univariate linear modelling function
Determination of the cost function
Minimizing the cost function
Example section
Example 1 - univariate linear regression
Example - multivariate linear regression
Summary
Chapter 4: Logistic Regression
Problem description
Logistic function predecessor - the logit functions
The logistic function
Example 1 - univariate logistic regression
Example 2 - Univariate logistic regression with skflow
Summary
Chapter 5: Simple FeedForward Neural Networks
Preliminary concepts
First project - Non linear synthetic function regression
Second project - Modeling cars fuel efficiency with non linear regression
Third project - Learning to classify wines: Multiclass classification
Summary
Chapter 6: Convolutional Neural Networks
Origin of convolutional neural networks
Example 1 - MNIST digit classification
Example 2 - image classification with the CIFAR10 dataset
Summary
Chapter 7: Recurrent Neural Networks and LSTM
Recurrent neural networks
Example 1 - univariate time series prediction with energy consumption data
Example 2 - writing music "a la" Bach
Summary
Chapter 8: Deep Neural Networks
Deep neural network definition
Deep network architectures through time
Alexnet
Inception v3
Residual Networks (ResNet)
Example - painting with style - VGG style transfer
Summary
Chapter 9: Running Models at Scale – GPU and Serving
GPU support on TensorFlow
Example 1 - assigning an operation to the GPU
Example 2 - calculating Pi number in parallel
Distributed TensorFlow
Example 3 - distributed Pi calculation
Example 4 - running a distributed model in a cluster
Summary
Chapter 10: Library Installation and Additional Tips
Linux installation
Windows installation
MacOS X installation
Summary

Book Details

ISBN 139781786466587
Paperback282 pages
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