Book Image

Applied Deep Learning with Keras

By : Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
5 (1)
Book Image

Applied Deep Learning with Keras

5 (1)
By: Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Overview of this book

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.
Table of Contents (12 chapters)
Applied Deep Learning with Keras
Preface
Preface

Summary


In this chapter, we learned about sequential modeling and sequential memory by examining some real-life cases with Google Assistant. We further learned how sequential modeling is related to RNNs. We also learned how RNNs are different from traditional feedforward networks. We learned about the vanishing gradient problem in detail, and learned how using an LSTM is better than a simple RNN to overcome the vanishing gradient problem. We applied the learning to time series problems by predicting stock trends.

In this book, we learned the basics of machine learning and Python, while also gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. We understood the difference between machine and deep learning. We learned how to build a logistic regression model, first with scikit-learn, and then with Keras. We further explored Keras and its different models by creating prediction models for various real-world scenarios, such as disease prediction. Then...