English | 2020 | ISBN-13: 978-1800562967 | 495 Pages | True (PDF, EPUB, MOBI) + Code| 58.44 MB

Discover how to leverage Keras, the powerful and easy-to-use open-source Python library for developing and evaluating deep learning models
Key Features

Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores
Explore advanced concepts such as sequential memory and sequential modeling
Reinforce your skills with real-world development, screencasts, and knowledge checks

Book Description

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.

This 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 interactive 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 neural network models.
What you will learn

Gain insights into the fundamentals of neural networks
Understand the limitations of machine learning and how it differs from deep learning
Build image classifiers with convolutional neural networks
Evaluate, tweak, and improve your models with techniques such as cross-validation
Create prediction models to detect data patterns and make predictions
Improve model accuracy with L1, L2, and dropout regularization

Who This Book Is For

If you know the basics of data science and machine learning, and want to get started with advanced machine learning technologies, such as artificial neural networks and deep learning, this workshop makes it easy. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.