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

Summary


In this chapter, we have covered how to prepare data and construct machine learning models. We have achieved this utilizing Python and libraries such as pandas and scikit-learn. We have also used the algorithms in scikit-learn to build our machine learning models.

In this chapter, we learned how to load data into Python, and how to manipulate data so that a machine learning model can be trained on the data. This involved converting all columns to numerical data types. We also learned to create a basic logistic regression classification model using scikit-learn algorithms. We divided the dataset into training and test datasets and fit the model to the training dataset. We evaluated the performance of the model on the test dataset using the model evaluation metrics: accuracy, precision, recall, and f1 score.

Finally, we iterated on this basic model by creating two models with different types of regularization to the model. We utilized cross-validation to determine the optimal parameter to use for the regularization parameter.

In the next chapter, we will use the same concept learned in this chapter; however, we will create the model using the Keras library. We will use the same dataset, and attempt to predict the same target value, for the same classification task. We will cover how to use regularization, cross-validation, and model evaluation metrics when fitting our neural network to the data.