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TensorFlow Machine Learning Projects

By : Jain, Amita Kapoor

3.7 (11)

TensorFlow Machine Learning Projects

3.7 (11)

By: Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

Overview of TensorFlow and Machine Learning

Overview of TensorFlow and Machine Learning

What is TensorFlow?

The TensorFlow core

Computation graph

Machine learning, classification, and logistic regression

Logistic regression with TensorFlow

Logistic regression with Keras

Summary

Questions

Further reading

Using Machine Learning to Detect Exoplanets in Outer Space

Using Machine Learning to Detect Exoplanets in Outer Space

What is a decision tree?

Why do we need ensembles?

Decision tree-based ensemble methods

Decision tree-based ensembles in TensorFlow

Detecting exoplanets in outer space

Building a TFBT model for exoplanet detection

Summary

Questions

Further reading

Sentiment Analysis in Your Browser Using TensorFlow.js

Sentiment Analysis in Your Browser Using TensorFlow.js

Understanding TensorFlow.js

Understanding Adam Optimization

Understanding categorical cross entropy loss

Understanding word embeddings

Building the sentiment analysis model

Running the model on a browser using TensorFlow.js

Summary

Questions

Digit Classification Using TensorFlow Lite

Digit Classification Using TensorFlow Lite

What is TensorFlow Lite?

Classification Model Evaluation Metrics

Classifying digits using TensorFlow Lite

Summary

Questions

Speech to Text and Topic Extraction Using NLP

Speech to Text and Topic Extraction Using NLP

Speech-to-text frameworks and toolkits

Google Speech Commands Dataset

Neural network architecture

Training the model

Summary

Questions

Further reading

Predicting Stock Prices using Gaussian Process Regression

Predicting Stock Prices using Gaussian Process Regression

Understanding Bayes' rule

Introducing Bayesian inference

Introducing Gaussian processes

Applying GPs to stock market prediction

Creating a stock price prediction model

Understanding the results obtained

Summary

Questions

Credit Card Fraud Detection using Autoencoders

Credit Card Fraud Detection using Autoencoders

Understanding auto-encoders

Building a fraud detection model

Summary

Questions

Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks

Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks

Understanding Bayesian deep learning

Understanding TensorFlow probability, variational inference, and Monte Carlo methods

Building a Bayesian neural network

Summary

Questions

Generating Matching Shoe Bags from Shoe Images Using DiscoGANs

Generating Matching Shoe Bags from Shoe Images Using DiscoGANs

Understanding generative models

Understanding DiscoGANs

Building a DiscoGAN model

Summary

Questions

Classifying Clothing Images using Capsule Networks

Classifying Clothing Images using Capsule Networks

Understanding the importance of capsule networks

Understanding capsules

The dynamic routing algorithm

CapsNet for classifying Fashion MNIST images

Training and testing the model

Reconstructing sample images

Limitations of capsule networks

Summary

Making Quality Product Recommendations Using TensorFlow

Making Quality Product Recommendations Using TensorFlow

Recommendation systems

Content-based filtering

Collaborative filtering

Hybrid systems

Matrix factorization

Introducing the Retailrocket dataset

Exploring the Retailrocket dataset

Pre-processing the data

The matrix factorization model for Retailrocket recommendations

The neural network model for Retailrocket recommendations

Summary

Questions

Further reading

Object Detection at a Large Scale with TensorFlow

Object Detection at a Large Scale with TensorFlow

Introducing Apache Spark

Understanding distributed TensorFlow

Learning about TensorFlowOnSpark

Object detection using TensorFlowOnSpark and Sparkdl

Summary

Generating Book Scripts Using LSTMs

Generating Book Scripts Using LSTMs

Understanding recurrent neural networks

Pre-processing the data

Defining the model

Training the model

Defining and training a text-generating model

Generating book scripts

Summary

Questions

Playing Pacman Using Deep Reinforcement Learning

Playing Pacman Using Deep Reinforcement Learning

Reinforcement learning

Reinforcement learning versus supervised and unsupervised learning

Components of Reinforcement Learning

OpenAI Gym

Creating a Pacman game in OpenAI Gym

DQN for deep reinforcement learning

Applying DQN to a game

Summary

Further Reading

What is Next?

What is Next?

Implementing TensorFlow in production

Recommendations for building AI applications

Limitations of deep learning

AI applications in industries

Ethical considerations in AI

Summary

Other Books You May Enjoy

Other Books You May Enjoy

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Overview of TensorFlow and Machine Learning

TensorFlow is a popular library for implementing machine learning-based solutions. It includes a low-level API known as TensorFlow core and many high-level APIs, including two of the most popular ones, known as TensorFlow Estimators and Keras. In this chapter, we will learn about the basics of TensorFlow and build a machine learning model using logistic regression to classify handwritten digits as an example.

We will cover the following topics in this chapter:

TensorFlow core:
- Tensors in TensorFlow core
- Constants
- Placeholders
- Operations
- Tensors from Python objects
- Variables
- Tensors from library functions

Computation graphs:
- Lazy loading and execution order
- Graphs on multiple devices – CPU and GPGPU
- Working with multiple graphs
Machine learning, classification, and logistic regression
Logistic regression examples in TensorFlow
Logistic regression examples in Keras

You can follow the code examples in this chapter by using the Jupyter Notebook named ch-01_Overview_of_TensorFlow_and_Machine_Learning.ipynb that's included in the code bundle.

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Programming languages

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