Book Image

Applied Deep Learning with Keras

By : Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
5 (1)
Book Image

Applied Deep Learning with Keras

5 (1)
By: Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Overview of this book

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.
Table of Contents (12 chapters)
Applied Deep Learning with Keras
Preface
Preface

Convolutional Neural Networks


When you talk about computer vision, you talk about CNNs in the same breath. A CNN is a class of deep neural network that is mostly used in the field of computer vision and imaging. CNNs are used to identify images, cluster them by their similarity, and implement object recognition within scenes. A CNN has different layers, namely the input layer, the output layer, and multiple hidden layers. These hidden layers of a CNN consist of fully connected layers, convolutional layers, a RELU layer as an activation function, normalization layers, and pooling layers. On a very simple level, CNNs help to identify images and label them appropriately; for example, a tiger image will be identified as a tiger:

Figure 7.1: A Generalized CNN

An example of a CNN classifying a tiger:

Figure 7.2: CNN classifying a tiger