The Deep Learning with Keras Workshop

By : Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat

1 (1)

Buy this Book

The Deep Learning with Keras Workshop

1 (1)

By: Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat

Buy this Book

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.

Preface

About the Book

1. Introduction to Machine Learning with Keras

Introduction

Data Representation

Data Preprocessing

Life Cycle of Model Creation

scikit-learn

Keras

Model Training

Model Tuning

Summary

Free Chapter

2. Machine Learning versus Deep Learning

Introduction

Linear Transformations

Introduction to Keras

Summary

3. Deep Learning with Keras

Introduction

Building Your First Neural Network

Model Evaluation

Summary

4. Evaluating Your Model with Cross-Validation Using Keras Wrappers

Introduction

Cross-Validation

Cross-Validation for Deep Learning Models

Model Selection with Cross-Validation

Summary

5. Improving Model Accuracy

Introduction

Regularization

L1 and L2 Regularization

Dropout Regularization

Other Regularization Methods

Hyperparameter Tuning with scikit-learn

Summary

6. Model Evaluation

Summary

7. Computer Vision with Convolutional Neural Networks

Introduction

Computer Vision

Convolutional Neural Networks

The Architecture of a CNN

Image Augmentation

Summary

8. Transfer Learning and Pre-Trained Models

Introduction

Pre-Trained Sets and Transfer Learning

Fine-Tuning a Pre-Trained Network

Summary

9. Sequential Modeling with Recurrent Neural Networks

Introduction

Sequential Memory and Sequential Modeling

Recurrent Neural Networks (RNNs)

Long Short-Term Memory (LSTM)

Summary

Appendix

1. Introduction to Machine Learning with Keras

2. Machine Learning versus Deep Learning

3. Deep Learning with Keras

4. Evaluating 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

Customer Reviews

1 (1)

5 star

4 star

3 star

2 star

1 star

100%

L1 and L2 Regularization

The most common type of regularization for deep learning models is the one that keeps the weights of the network small. This type of regularization is called weight regularization and has two different variations: L2 regularization and L1 regularization. In this section, you will learn about these regularization methods in detail, along with how to implement them in Keras. Additionally, you will practice applying them to real-life problems and observe how they can improve the performance of a model.