Book Image

Applied Deep Learning with Keras

By : Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
Book Image

Applied Deep Learning with Keras

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

Recurrent Neural Networks (RNNs)


RNNs are a class of neural networks that are built on the concept of sequential memory. Unlike in traditional neural networks, an RNN predicts the results in sequential data. Currently, an RNN is the most robust and powerful technique available for processing sequential data.

If you have access to a smartphone that has Google Assistant, try opening it and asking the question: "When was the United Nations formed?". The answer is displayed in the following screenshot:

Figure 9.2: Google Assistant's output

Now ask a second question, "Why was it formed?", as follows:

Figure 9.3: Google Assistant's contextual output

Now ask a third question, "Where are its headquarters?", and you should get the following answer:

Figure 9.4: Google Assistant's output

One interesting thing to note here is that we only mentioned "United Nations" in the first question. In the second and third question, we simply asked the assistant why it was formed and where the headquarters were, respectively...