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

Introduction


Neural networks are the building blocks of all deep learning models. In traditional neural networks, all the inputs and outputs are independent. However, there are instances where a particular output is dependent on the previous output of the system. Consider the stock price of a company as an example – the output at the end of any given day is related to the output of the previous day. Similarly, in Natural Language Processing (NLP), the final words in a sentence are dependent on the previous words in the sentence. A special type of neural network, called a Recurrent Neural Network (RNN), is used to solve these types of problems where the network needs to remember previous outputs. This chapter introduces and explores the concepts and applications of RNNs. It also explains how RNNs are different from standard feedforward neural networks. You will also gain an understanding of what the vanishing gradient problem is and a Long-Short-Term-Memory (LSTM) network. This chapter also...