Applied Deep Learning with Keras: Solve complex real-life problems with the simplicity of Keras (English Edition)

Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API.

Key Features

Solve complex machine learning problems with precision

Evaluate, tweak, and improve your deep learning models and solutions

Use different types of neural networks to solve real-world problems

Book Description

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.

What you will learn

Understand the difference between single-layer and multi-layer neural network models

Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks

Apply L1, L2, and dropout regularization to improve the accuracy of your model

Implement cross-validate using Keras wrappers with scikit-learn

Understand the limitations of model accuracy

Who this book is for

If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus. Table of Contents

Introduction to Machine Learning with Keras

Machine Learning versus Deep Learning

Deep Learning with Keras

Evaluate your Model with Cross Validation with Keras Wrappers

Improving Model Accuracy

Model Evaluation

Computer Vision with Convolutional Neural Networks

Transfer Learning and Pre-Trained Models

Sequential Modeling with Recurrent Neural Network

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