Predictive Analytics with TensorFlow

Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.
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Predictive Analytics with TensorFlow

Md. Rezaul Karim

3 customer reviews
Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.
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Book Details

ISBN 139781788398923
Paperback522 pages

Book Description

Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence.

This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling.

The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system.

The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis.

Table of Contents

Chapter 1: Basic Python and Linear Algebra for Predictive Analytics
A basic introduction to predictive analytics
A bit of linear algebra
Installing and getting started with Python
Getting started with Python
Vectors, matrices, and graphs
Span and linear independence
Principal component analysis
Singular value decomposition
Predictive analytics tools in Python
Summary
Chapter 2: Statistics, Probability, and Information Theory for Predictive Modeling
Using statistics in predictive modeling
Basic probability for predictive modeling
Using information theory in predictive modeling
Summary
Chapter 3: From Data to Decisions – Getting Started with TensorFlow
Taking decisions based on data - Titanic example
General overview of TensorFlow
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow programming model
Data model in TensorFlow
TensorBoard
Getting started with TensorFlow – linear regression and beyond
Summary
Chapter 4: Putting Data in Place - Supervised Learning for Predictive Analytics
Supervised learning for predictive analytics
Linear regression - revisited
From disaster to decision - Titanic example revisited
Summary
Chapter 5: Clustering Your Data - Unsupervised Learning for Predictive Analytics
Unsupervised learning and clustering
Using K-means for predictive analytics
Predictive models for clustering audio files
Using kNN for predictive analytics
Summary
Chapter 6: Predictive Analytics Pipelines for NLP
NLP analytics pipelines
Transformers and estimators
Using BOW for predictive analytics
TF-IDF model for predictive analytics
Using Word2vec for sentiment analysis
Summary
Chapter 7: Using Deep Neural Networks for Predictive Analytics
Deep learning for better predictive analytics
Artificial Neural Networks
Deep Neural Networks
Multilayer perceptrons
DNN performance analysis
Fine-tuning DNN hyperparameters
Using multilayer perceptrons for predictive analytics
Deep belief networks
Using deep belief networks for predictive analytics
Summary
Chapter 8: Using Convolutional Neural Networks for Predictive Analytics
CNNs and the drawbacks of regular DNNs
CNN architecture
Convolutional operations
Pooling layer and padding operations
Tuning CNN hyperparameters
CNN-based predictive model for sentiment analysis
CNN model for emotion recognition
CNN predictive model for image classification
Summary
Chapter 9: Using Recurrent Neural Networks for Predictive Analytics
RNN architecture
Using BRNN for image classification
Implementing an RNN for spam prediction
Developing a predictive model for time series data
An LSTM predictive model for sentiment analysis
Summary
Chapter 10: Recommendation Systems for Predictive Analytics
Recommendation systems
Collaborative filtering approach for movie recommendations
Factorization machines for recommendation systems
Improved factorization machines for predictive analytics
Summary
Chapter 11: Using Reinforcement Learning for Predictive Analytics
Reinforcement learning
Reinforcement learning in predictive analytics
Notation, policy, and utility in RL
Developing a multiarmed bandit's predictive model
Developing a stock price predictive model
Summary

What You Will Learn

  • Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling
  • Develop predictive models using classification, regression, and clustering algorithms
  • Develop predictive models for NLP
  • Learn how to use reinforcement learning for predictive analytics
  • Factorization Machines for advanced recommendation systems
  • Get a hands-on understanding of deep learning architectures for advanced predictive analytics
  • Learn how to use deep Neural Networks for predictive analytics
  • See how to use recurrent Neural Networks for predictive analytics
  • Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysis

Authors

Table of Contents

Chapter 1: Basic Python and Linear Algebra for Predictive Analytics
A basic introduction to predictive analytics
A bit of linear algebra
Installing and getting started with Python
Getting started with Python
Vectors, matrices, and graphs
Span and linear independence
Principal component analysis
Singular value decomposition
Predictive analytics tools in Python
Summary
Chapter 2: Statistics, Probability, and Information Theory for Predictive Modeling
Using statistics in predictive modeling
Basic probability for predictive modeling
Using information theory in predictive modeling
Summary
Chapter 3: From Data to Decisions – Getting Started with TensorFlow
Taking decisions based on data - Titanic example
General overview of TensorFlow
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow programming model
Data model in TensorFlow
TensorBoard
Getting started with TensorFlow – linear regression and beyond
Summary
Chapter 4: Putting Data in Place - Supervised Learning for Predictive Analytics
Supervised learning for predictive analytics
Linear regression - revisited
From disaster to decision - Titanic example revisited
Summary
Chapter 5: Clustering Your Data - Unsupervised Learning for Predictive Analytics
Unsupervised learning and clustering
Using K-means for predictive analytics
Predictive models for clustering audio files
Using kNN for predictive analytics
Summary
Chapter 6: Predictive Analytics Pipelines for NLP
NLP analytics pipelines
Transformers and estimators
Using BOW for predictive analytics
TF-IDF model for predictive analytics
Using Word2vec for sentiment analysis
Summary
Chapter 7: Using Deep Neural Networks for Predictive Analytics
Deep learning for better predictive analytics
Artificial Neural Networks
Deep Neural Networks
Multilayer perceptrons
DNN performance analysis
Fine-tuning DNN hyperparameters
Using multilayer perceptrons for predictive analytics
Deep belief networks
Using deep belief networks for predictive analytics
Summary
Chapter 8: Using Convolutional Neural Networks for Predictive Analytics
CNNs and the drawbacks of regular DNNs
CNN architecture
Convolutional operations
Pooling layer and padding operations
Tuning CNN hyperparameters
CNN-based predictive model for sentiment analysis
CNN model for emotion recognition
CNN predictive model for image classification
Summary
Chapter 9: Using Recurrent Neural Networks for Predictive Analytics
RNN architecture
Using BRNN for image classification
Implementing an RNN for spam prediction
Developing a predictive model for time series data
An LSTM predictive model for sentiment analysis
Summary
Chapter 10: Recommendation Systems for Predictive Analytics
Recommendation systems
Collaborative filtering approach for movie recommendations
Factorization machines for recommendation systems
Improved factorization machines for predictive analytics
Summary
Chapter 11: Using Reinforcement Learning for Predictive Analytics
Reinforcement learning
Reinforcement learning in predictive analytics
Notation, policy, and utility in RL
Developing a multiarmed bandit's predictive model
Developing a stock price predictive model
Summary

Book Details

ISBN 139781788398923
Paperback522 pages
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From 3 reviews

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