Matthew Moocarme - The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-world datasets
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Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities
Key Features- Understand the fundamentals of tensors, neural networks, and deep learning
- Discover how to implement and fine-tune deep learning models for real-world datasets
- Build your experience and confidence with hands-on exercises and activities
Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging.
If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, itll quickly get you up and running.
Youll start off with the basics learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, youll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models.
Building on this solid foundation, youll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.
By the end of this deep learning book, youll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
What you will learn- Get to grips with TensorFlows mathematical operations
- Pre-process a wide variety of tabular, sequential, and image data
- Understand the purpose and usage of different deep learning layers
- Perform hyperparameter-tuning to prevent overfitting of training data
- Use pre-trained models to speed up the development of learning models
- Generate new data based on existing patterns using generative models
This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.
Table of Contents- Introduction to Machine Learning with TensorFlow
- Loading and Processing Data
- TensorFlow Development
- Regression and Classification Models
- Classification Models
- Regularization and Hyperparameter Tuning
- Convolutional Neural Networks
- Pre-Trained Networks
- Recurrent Neural Networks
- Custom TensorFlow Components
- Generative Models
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A hands-on guide to building deep learning models from scratch using real-world datasets
Matthew Moocarme, Anthony So, and Anthony Maddalone
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Authors: Matthew Moocarme, Anthony So, and Anthony Maddalone
Reviewer: Abhranshu Bagchi
Managing Editor: Prachi Jain
Acquisitions Editors: Royluis Rodrigues, Kunal Sawant, and Sneha Shinde
Production Editor: Salma Patel
Editorial Board: Megan Carlisle, Heather Gopsill, Manasa Kumar, Alex Mazonowicz, Monesh Mirpuri, Bridget Neale, Abhishek Rane, Brendan Rodrigues, Ankita Thakur, Nitesh Thakur, and Jonathan Wray
First published: December 2021
Production reference: 1141221
ISBN: 978-1-80020-525-3
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
If you want to learn to build deep learning models in TensorFlow to solve real-world problems, then this is the book for you.
Beginning with an introduction to TensorFlow, this book gives you a tour of the basic mathematical operations of tensors, as well as various methods of data-preparation for modeling and time-saving model-development using TensorFlow resources. You will build regression and classification models, use regularization to prevent models from overfitting training data, and create convolutional neural networks to solve classification tasks on image datasets. Finally, you'll learn to implement pre-trained, recurrent, and generative models and create your own custom TensorFlow components to use within your models.
By the end of this book, you'll have the practical skills to build, train, and evaluate deep learning models using the TensorFlow framework.
Matthew Moocarme is an accomplished data scientist with more than eight years of experience in creating and utilizing machine learning models. He comes from a background in the physical sciences, in which he holds a Ph.D. in physics from the Graduate Center of CUNY. Currently, he leads a team of data scientists and engineers in the media and advertising space to build and integrate machine learning models for a variety of applications. In his spare time, Matthew enjoys sharing his knowledge with the data science community through published works, conference presentations, and workshops.
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