Applied Deep Learning with Keras

Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat , Mahla Abdolahnejad , Matthew Moocarme 지음

Packt(GCO Science) 2019년 04월 24일 출간

작품소개

책소개

▶Book Description
Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

▶What You Will Learn
？ Understand the difference between single-layer and multi-layer neural network models
？ Use Keras to build simple logistic regression models, deep neural networks, recurrent neural ？ networks, and convolutional neural networks
？ Apply L1, L2, and dropout regularization to improve the accuracy of your model
？ Implement cross-validate using Keras wrappers with scikit-learn
？ Understand the limitations of model accuracy

▶Key Features
？ Solve complex machine learning problems with precision
？ Evaluate, tweak, and improve your deep learning models and solutions
？ Use different types of neural networks to solve real-world problems

목차

▶TABLE of CONTENTS
1 Introduction to Machine Learning with Keras
2 Machine Learning versus Deep Learning
3 Deep Learning with Keras
4 Evaluate Your Model with Cross-Validation using Keras Wrappers
5 Improving Model Accuracy
6 Model Evaluation
7 Computer Vision with Convolutional Neural Networks
8 Transfer Learning and Pre-Trained Models
9 Sequential Modeling with Recurrent Neural Networks

책 속으로

▶Who This Book Is For
If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus.

▶Audience
If you have basic knowledge about data science and machine learning and want to upgrade your skills to learn about artificial neural networks and deep learning, you can accomplish a lot with this book. Prior experience of programming in Python and familiarity with statistics and logistic regression will help you get the most out of this book. Though not necessary, it will be an added bonus if you are familiar with the scikitlearn library.

▶Approach
Applied Deep Learning with Keras takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. The book contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

출판사 서평

▶ About the Book
Applied Deep Learning with Keras takes you from a basic level of knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. To understand the difference between machine and deep learning, you will build a logistic regression model with scikit-learn and then with Keras. By building prediction models for several real-world scenarios, such as disease prediction and customer churn prediction, you will dive deep into Keras and its many models. You will also gain knowledge about how to evaluate, optimize, and improve your models to gain maximum information. You will learn how to use Keras wrappers with scikit-learn and implement cross-validation techniques on your findings, and apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To improve accuracy, you will learn how to apply the null accuracy, precision, sensitivity, specificity, and AUC-ROC score techniques to fine-tune your model. Then, you will explore convolutional and recurrent neural networks in detail.

작가정보

저자(글) Ritesh Bhagwat

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.

저자(글) Mahla Abdolahnejad

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.

저자(글) Matthew Moocarme

Matthew Moocarme is a director and senior data scientist in Viacom's Advertising Science team. As a data scientist at Viacom, he designs data-driven solutions to help Viacom gain insights, streamline workflows, and solve complex problems using data science and machine learning. Matthew lives in New York City and outside of work enjoys combining deep learning with music theory. He is a classically-trained physicist, holding a Ph.D. in Physics from The Graduate Center of CUNY and is an active Artificial Intelligence developer, researcher, practitioner, and educator.