The Deep Learning with Keras Workshop
Learn how to define and train neural network models with just a few lines of code
Published on 29. July 2020
Book
Paperback/Softback
496 pages
978-1-80107-118-5 (ISBN)
Description
Discover how to leverage Keras, the powerful and easy-to-use open-source Python library for developing and evaluating deep learning models
Key Features
Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores
Explore advanced concepts such as sequential memory and sequential modeling
Reinforce your skills with real-world development, screencasts, and knowledge checks
Book DescriptionNew experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.
What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.
The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data.
By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.What you will learn
Gain insights into the fundamentals of neural networks
Understand the limitations of machine learning and how it differs from deep learning
Build image classifiers with convolutional neural networks
Evaluate, tweak, and improve your models with techniques such as cross-validation
Create prediction models to detect data patterns and make predictions
Improve model accuracy with L1, L2, and dropout regularization
Who this book is forIf you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.
Key Features
Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores
Explore advanced concepts such as sequential memory and sequential modeling
Reinforce your skills with real-world development, screencasts, and knowledge checks
Book DescriptionNew experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.
What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.
The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data.
By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.What you will learn
Gain insights into the fundamentals of neural networks
Understand the limitations of machine learning and how it differs from deep learning
Build image classifiers with convolutional neural networks
Evaluate, tweak, and improve your models with techniques such as cross-validation
Create prediction models to detect data patterns and make predictions
Improve model accuracy with L1, L2, and dropout regularization
Who this book is forIf you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-80107-118-5 (9781801071185)
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
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. Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans. Ritesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.
Content
Table of Contents
Introduction to Machine Learning with Keras
Machine Learning versus Deep Learning
Deep Learning with Keras
Evaluating your Model with Cross-Validation Using Keras Wrappers
Improving Model Accuracy
Model Evaluation
Computer Vision with Convolutional Neural Networks
Transfer Learning and Pre-Trained Models
Sequential Modeling with Recurrent Neural Networks
Introduction to Machine Learning with Keras
Machine Learning versus Deep Learning
Deep Learning with Keras
Evaluating your Model with Cross-Validation Using Keras Wrappers
Improving Model Accuracy
Model Evaluation
Computer Vision with Convolutional Neural Networks
Transfer Learning and Pre-Trained Models
Sequential Modeling with Recurrent Neural Networks