
TensorFlow Machine Learning Projects
Build 13 real-world projects with advanced numerical computations using the Python ecosystem
Packt Publishing
Published on 30. November 2018
Book
Paperback/Softback
322 pages
978-1-78913-221-2 (ISBN)
Description
Implement TensorFlow\'s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects
Key Features
Use machine learning and deep learning principles to build real-world projects
Get to grips with TensorFlow's impressive range of module offerings
Implement projects on GANs, reinforcement learning, and capsule network
Book DescriptionTensorFlow 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.
What you will learn
Understand the TensorFlow ecosystem using various datasets and techniques
Create recommendation systems for quality product recommendations
Build projects using CNNs, NLP, and Bayesian neural networks
Play Pac-Man using deep reinforcement learning
Deploy scalable TensorFlow-based machine learning systems
Generate your own book script using RNNs
Who this book is forTensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
Key Features
Use machine learning and deep learning principles to build real-world projects
Get to grips with TensorFlow's impressive range of module offerings
Implement projects on GANs, reinforcement learning, and capsule network
Book DescriptionTensorFlow 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.
What you will learn
Understand the TensorFlow ecosystem using various datasets and techniques
Create recommendation systems for quality product recommendations
Build projects using CNNs, NLP, and Bayesian neural networks
Play Pac-Man using deep reinforcement learning
Deploy scalable TensorFlow-based machine learning systems
Generate your own book script using RNNs
Who this book is forTensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 18 mm
Weight
604 gr
ISBN-13
978-1-78913-221-2 (9781789132212)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Ankit Jain | Amita Kapoor
TensorFlow Machine Learning Projects
Build 13 real-world projects with advanced numerical computations using the Python ecosystem
E-Book
09/2024
Packt Publishing
from
€39.29
Available for download
Persons
Ankit Jain currently works as a senior research scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber's problems, ranging from forecasting and food delivery to self-driving cars. Previously, he has worked in a variety of data science roles at the Bank of America, Facebook, and other start-ups. He has been a featured speaker at many of the top AI conferences and universities, including UC Berkeley, O'Reilly AI conference, and others. He has a keen interest in teaching and has mentored over 500 students in AI through various start-ups and bootcamps. He completed his MS at UC Berkeley and his BS at IIT Bombay (India). ?Amita Kapoor, a seasoned expert in Artificial Intelligence, serves as the Chief Artificial Intelligence Officer at Retured, bringing over 25 years of experience in AI, data science, and neuroscience. Her consultancy, NePeur, stands testament to her leadership in applying AI across diverse industries, enhancing operational efficiency and business intelligence. Amita is also a devoted board member of Neuromatch Academy, where she plays a crucial role in making neuroscience and deep learning education accessible to all. Holding a PhD from the University of Delhi, she has dedicated her career to education, authoring numerous articles and papers, and creating impactful online classes. Her significant contributions extend to pioneering projects in intelligent vehicle fleet management, home surveillance through AI-powered face detection, and robust data anonymization solutions. Connect with Amita on LinkedIn.
Content
Table of Contents
Overview of Tensorflow and Machine Learning
Using Machine Learning to detect exoplanets in outer space
Sentiment Analysis in your browser using Tensorflow.js
Digit Classification using Tensorflow Lite
Speech to text and topic extraction using NLP
Predicting Stock Prices using Gaussian Process Regression
Credit Card Fraud Detection using Autoencoders
Generating Uncertainty in Traffic Signs Classifier using Bayesian Neural Networks
Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
Classifying Clothing Images using Capsule Networks
Making Quality Product Recommendations Using TensorFlow
Object detection at a large scale with Tensorflow
Generating Book Scripts Using LSTMs
Playing Pacman using Deep Reinforcement Learning
What is next?
Overview of Tensorflow and Machine Learning
Using Machine Learning to detect exoplanets in outer space
Sentiment Analysis in your browser using Tensorflow.js
Digit Classification using Tensorflow Lite
Speech to text and topic extraction using NLP
Predicting Stock Prices using Gaussian Process Regression
Credit Card Fraud Detection using Autoencoders
Generating Uncertainty in Traffic Signs Classifier using Bayesian Neural Networks
Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
Classifying Clothing Images using Capsule Networks
Making Quality Product Recommendations Using TensorFlow
Object detection at a large scale with Tensorflow
Generating Book Scripts Using LSTMs
Playing Pacman using Deep Reinforcement Learning
What is next?