Applied Deep Learning with Keras

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

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.