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The TensorFlow Workshop
The TensorFlow Workshop

The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-world datasets

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Profile Icon Matthew Moocarme Profile Icon Abhranshu Bagchi Profile Icon Anthony So Profile Icon Maddalone
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Can$55.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (25 Ratings)
Paperback Dec 2021 600 pages 1st Edition
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Can$39.99 Can$44.99
Paperback
Can$55.99
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Free Trial
Arrow left icon
Profile Icon Matthew Moocarme Profile Icon Abhranshu Bagchi Profile Icon Anthony So Profile Icon Maddalone
Arrow right icon
Can$55.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (25 Ratings)
Paperback Dec 2021 600 pages 1st Edition
eBook
Can$39.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial
eBook
Can$39.99 Can$44.99
Paperback
Can$55.99
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Free Trial

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The TensorFlow Workshop

2. Loading and Processing Data

Overview

In this chapter, you will learn how to load and process a variety of data types for modeling in TensorFlow. You will implement methods to input data into TensorFlow models so that model training can be optimized.

By the end of this chapter, you will know how to input tabular data, images, text, and audio data and preprocess them so that they are suitable for training TensorFlow models.

Introduction

In the previous chapter, you learned how to create, utilize, and apply linear transformations to tensors using TensorFlow. The chapter started with the definition of tensors and how they can be created using the Variable class in the TensorFlow library. You then created tensors of various ranks and learned how to apply tensor addition, reshaping, transposition, and multiplication using the library. These are all examples of linear transformations. You concluded that chapter by covering optimization methods and activation functions and how they can be accessed in the TensorFlow library.

When training machine learning models in TensorFlow, you must supply the model with training data. The raw data that is available may come in a variety of formats—for example, tabular CSV files, images, audio, or text files. Different data sources are loaded and preprocessed in different ways in order to provide numerical tensors for TensorFlow models. For example, virtual assistants use voice queries as input interaction and then apply machine learning models to decipher input speech and perform specific actions as output. To create the models for this task, the audio data of the speech input must be loaded into memory. A preprocessing step also needs to be involved that converts the audio input into text. Following this, the text is converted into numerical tensors for model training. This is one example that demonstrates the complexity of creating models from non-tabular, non-numerical data such as audio data.

This chapter will explore a few of the common data types that are utilized for building machine learning models. You will load raw data into memory in an efficient manner, and then perform some preprocessing steps to convert the raw data into numerical tensors that are appropriate for training machine learning models. Luckily, machine learning libraries have advanced significantly, which means that training models with data types such as images, text, and audio is extremely accessible to practitioners.

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Key benefits

  • 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

Description

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, it’ll quickly get you up and running. You’ll 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, you’ll 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, you’ll 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, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.

Who is this book for?

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.

What you will learn

  • Get to grips with TensorFlow's 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
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Publication date : Dec 15, 2021
Length: 600 pages
Edition : 1st
Language : English
ISBN-13 : 9781800205253
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Length: 600 pages
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Language : English
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Table of Contents

11 Chapters
1. Introduction to Machine Learning with TensorFlow Chevron down icon Chevron up icon
2. Loading and Processing Data Chevron down icon Chevron up icon
3. TensorFlow Development Chevron down icon Chevron up icon
4. Regression and Classification Models Chevron down icon Chevron up icon
5. Classification Models Chevron down icon Chevron up icon
6. Regularization and Hyperparameter Tuning Chevron down icon Chevron up icon
7. Convolutional Neural Networks Chevron down icon Chevron up icon
8. Pre-Trained Networks Chevron down icon Chevron up icon
9. Recurrent Neural Networks Chevron down icon Chevron up icon
10. Custom TensorFlow Components Chevron down icon Chevron up icon
11. Generative Models Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(25 Ratings)
5 star 64%
4 star 28%
3 star 8%
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Atalay Denknalbant Dec 19, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In this book, there are decent explanations of neural network models and case studies based on those models which are tailor-made and help to understand models easily. Additionally, also book touches on every necessary TensorFlow class that widens readers' TensorFlow knowledge.
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Dwayne M Feb 16, 2022
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This book provides an in-depth perspective on all things TensorFlow from preprocessing to output. You learn how to avoid many mistakes that can cost valuable time and how to structure your models for optimal prediction accuracy and success. You will also learn about important topics such as transfer learning and custom components. I only wish this book covered deployment in greater detail.
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Dillon Grafton Feb 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Awesome presentation with concise easy to follow chapters. Great book!
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Ercim Caglasan Feb 08, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It has lots of insights and in depth knowledge of ML and deep learning consepts as well as well structured step by step hands on practical guides through tensorFlow framework. It covers all the basic algorithms and also covers important developments like nlp, transfer learning, optimization techniques and model measurements.It gives you ways to generate end to end pipeline from gathering and cleaning data and to building models and deployment. Although I would like to have more information on model developments in real production environments, this may come in the second edition :)Anyhow, good job on putting this all together.👍🏻
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Rustem Dec 21, 2021
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The book covers DL model architecture using Tensorflow with code examples. It would be useful for beginners but also as a reference for data scientists and ML engineers. Keep in mind that it is specifically focused on building neural networks and not on training or inference. Regarding network components, it covers most contemporary architectures though it doesn't cover transformers.
Amazon Verified review Amazon
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