Applied Deep Learning with Keras

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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