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, you learned about two very important groups of techniques for improving the accuracy of your deep learning models: regularization techniques and hyperparameter-tuning techniques. You learned about how regularization helps address the overfitting problem, and had an introduction to different regularization methods. Among those methods, L1 and L2 norm regularization and dropout regularization were covered in detail, since they are very important, commonly used regularization techniques. You also learned about the importance of hyperparameter tuning for machine learning models and saw how performing hyperparameter tuning is highly challenging for deep learning models in particular. You learned how to perform hyperparameter tuning on Keras models more easily using scikit-learn optimizers.

In the next chapter, you will learn about the limitations of accuracy metrics when evaluating model performance. You will also learn about other metrics, such as precision, sensitivity...