Book Image

The Deep Learning with Keras Workshop

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

The Deep Learning with Keras Workshop

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

Overview of this book

New 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.
Table of Contents (11 chapters)
Preface

5. Improving Model Accuracy

Overview

This chapter introduces the concept of regularization for neural networks. Regularization aims to prevent the model from overfitting the training data during the training process and provides more accurate results when the model is tested on new unseen data. You will learn to utilize different regularization techniques—L1 and L2 regularization and dropout regularization—to improve model performance. Regularization is an important component as it prevents neural networks from overfitting the training data and helps us build robust, accurate models that perform well on new, unseen data. By the end of this chapter, you will be able to implement a grid search and random search in scikit-learn and find the optimal hyperparameters.