The Deep Learning Workshop
Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
Published on 31. July 2020
Book
Paperback/Softback
474 pages
978-1-83921-985-6 (ISBN)
Description
Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text
Key Features
Understand how to implement deep learning with TensorFlow and Keras
Learn the fundamentals of computer vision and image recognition
Study the architecture of different neural networks
Book Description
Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.
The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.
By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.
What you will learn
Understand how deep learning, machine learning, and artificial intelligence are different
Develop multilayer deep neural networks with TensorFlow
Implement deep neural networks for multiclass classification using Keras
Train CNN models for image recognition
Handle sequence data and use it in conjunction with RNNs
Build a GAN to generate high-quality synthesized images
Who this book is for
If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.
Key Features
Understand how to implement deep learning with TensorFlow and Keras
Learn the fundamentals of computer vision and image recognition
Study the architecture of different neural networks
Book Description
Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.
The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.
By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.
What you will learn
Understand how deep learning, machine learning, and artificial intelligence are different
Develop multilayer deep neural networks with TensorFlow
Implement deep neural networks for multiclass classification using Keras
Train CNN models for image recognition
Handle sequence data and use it in conjunction with RNNs
Build a GAN to generate high-quality synthesized images
Who this book is for
If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 26 mm
Weight
877 gr
ISBN-13
978-1-83921-985-6 (9781839219856)
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
Mirza Rahim Baig | Thomas V. Joseph | Nipun Sadvilkar
The Deep Learning Workshop
Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
E-Book
07/2020
Packt Publishing
€25.49
Available for download
Persons
Mirza Rahim Baig is an avid problem solver who uses deep learning and AI to solve business problems and create impact. A BITS, Pilani graduate, Rahim is a lead at Flipkart, India's largest e-commerce platform. He has a decade of experience in creating value from data, harnessing the power of the latest in Machine learning and AI. Rahim is also a teacher - designing, creating, teaching data science for various learning platforms. He loves making the complex easy to understand.
Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning tool sets across multiple industry segments.
Nipun Sadvilkar is a senior data scientist at US healthcare company leading a team of data scientists and subject matter expertise to design and build the clinical NLP engine to revamp medical coding workflows, enhance coder efficiency, and accelerate revenue cycle. He has experience of more than 3 years in building NLP solutions and web-based data science platforms in the area of healthcare, finance, media, and psychology. His interests lie at the intersection of machine learning and software engineering with a fair understanding of the business domain. He is a member of the regional and national python community. He is author of pySBD - an NLP open-source python library for sentence segmentation which is recognized by ExplosionAI (spaCy) and AllenAI (scispaCy) organizations.
Mohan Kumar Silaparasetty is seasoned deep learning and AI professional. He is a graduate from IIT Kharagpur with more than 25 years of industry experience in a variety of roles. After having a successful corporate career, Mohan embarked on his entrepreneurial journey and is the co-founder and CEO of Trendwise Analytics. This company provides consulting and training in AI and deep learning. He is also the organizer of the Bangalore Artificial intelligence Meetup group with over 3500 members.
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. He is also a successful coach and mentor with capabilities in statistical analysis and expertise in machine learning with Python.
Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning tool sets across multiple industry segments.
Nipun Sadvilkar is a senior data scientist at US healthcare company leading a team of data scientists and subject matter expertise to design and build the clinical NLP engine to revamp medical coding workflows, enhance coder efficiency, and accelerate revenue cycle. He has experience of more than 3 years in building NLP solutions and web-based data science platforms in the area of healthcare, finance, media, and psychology. His interests lie at the intersection of machine learning and software engineering with a fair understanding of the business domain. He is a member of the regional and national python community. He is author of pySBD - an NLP open-source python library for sentence segmentation which is recognized by ExplosionAI (spaCy) and AllenAI (scispaCy) organizations.
Mohan Kumar Silaparasetty is seasoned deep learning and AI professional. He is a graduate from IIT Kharagpur with more than 25 years of industry experience in a variety of roles. After having a successful corporate career, Mohan embarked on his entrepreneurial journey and is the co-founder and CEO of Trendwise Analytics. This company provides consulting and training in AI and deep learning. He is also the organizer of the Bangalore Artificial intelligence Meetup group with over 3500 members.
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. He is also a successful coach and mentor with capabilities in statistical analysis and expertise in machine learning with Python.
Content
Table of Contents
Building Blocks of Deep Learning
Neural Networks
Image Classification with Convolutional Neural Networks (CNNs)
Deep Learning for Text - Embeddings
Deep Learning for Sequences
LSTMs, GRUs, and Advanced RNNs
Generative Adversarial Networks
Building Blocks of Deep Learning
Neural Networks
Image Classification with Convolutional Neural Networks (CNNs)
Deep Learning for Text - Embeddings
Deep Learning for Sequences
LSTMs, GRUs, and Advanced RNNs
Generative Adversarial Networks