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Applied Deep Learning with Keras

Applied Deep Learning with Keras

By : Ritesh Bhagwat , Mahla Abdolahnejad , Matthew Moocarme
4.3 (7)
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Applied Deep Learning with Keras

Applied Deep Learning with Keras

4.3 (7)
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)
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Applied Deep Learning with Keras
Preface
Preface

Sequential Memory and Sequential Modeling


If we analyze the stock price of Apple for the past five months, as shown in Figure 9.1, we can see that there is a trend. To predict or forecast future stock prices, we need to gain an understanding of this trend and then do our mathematical computations while keeping this trend in mind:

Figure 9.1: Apple's stock price over the last five months

This trend is deeply related to sequential memory and sequential modeling. If you have a model that can remember the previous outputs and then predict the next output based on the previous outputs, we say that the model has sequential memory.

The modeling that is done to process this sequential memory is known as sequential modeling. This is not only true for stock market data, but it is also true in NLP applications; we will look at one such example later, when we study RNNs.

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