The The TensorFlow Workshop
A hands-on guide to building deep learning models from scratch using real-world datasets
Published on 15. December 2021
Book
Paperback/Softback
600 pages
978-1-80020-525-3 (ISBN)
Description
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
Book DescriptionGetting 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.
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
Who this book is forThis 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.
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
Book DescriptionGetting 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.
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
Who this book is forThis 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 32 mm
Weight
1104 gr
ISBN-13
978-1-80020-525-3 (9781800205253)
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
Matthew Moocarme | Abhranshu Bagchi | Anthony So So
The TensorFlow Workshop
A hands-on guide to building deep learning models from scratch using real-world datasets
E-Book
06/2024
1st Edition
Packt Publishing Limited
from
€31.19
Available for download
Persons
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. Anthony So is an outstanding leader with more than 13 years of experience. He is recognized for his analytical skills and data-driven approach for solving complex business problems and driving performance improvements. Anthony Maddalone is a research engineer at TieSet, a Silicon Valley based leader in distributed artificial intelligence and federated learning. He is a former founder and CEO of a successful e-commerce startup. He is also a master's candidate in analytics with a focus on industrial and systems engineering from the Georgia Institute of Technology.
Content
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
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