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The Deep Learning with Keras Workshop
The Deep Learning with Keras Workshop

The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code

By Matthew Moocarme , Mahla Abdolahnejad , Ritesh Bhagwat
$38.99
Book Jul 2020 496 pages 1st Edition
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Publication date : Jul 29, 2020
Length 496 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800562967
Category :
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The Deep Learning with Keras Workshop

2. Machine Learning versus Deep Learning

Overview

In this chapter, we will begin creating Artificial Neural Networks (ANNs) using the Keras library. Before utilizing the library for modeling, we will get an introduction to the mathematics that comprise ANNs—understanding linear transformations and how they can be applied in Python. You'll build a firm grasp of the mathematics that make up ANNs. By the end of this chapter, we will have applied that knowledge by building a logistic regression model with Keras.

Introduction

In the previous chapter, we discussed some applications of machine learning and even built models with the scikit-learn Python package. The previous chapter covered how to preprocess real-world datasets so that they can be used for modeling. To do this, we converted all the variables into numerical data types and converted categorical variables into dummy variables. We used the logistic regression algorithm to classify users of a website by their purchase intention from the online shoppers purchasing intention dataset. We advanced our model-building skills by adding regularization to the dataset to improve the performance of our models.

In this chapter, we will continue learning how to build machine learning models and extend our knowledge so that we can build an Artificial Neural Network (ANN) with the Keras package. (Remember that ANNs represent a large class of machine learning algorithms that are so-called because their architecture resembles the neurons in the...

Linear Transformations

In this section, we will introduce linear transformations. Linear transformations are the backbone of modeling with ANNs. In fact, all the processes of ANN modeling can be thought of as a series of linear transformations. The working components of linear transformations are scalars, vectors, matrices, and tensors. Operations such as addition, transposition, and multiplication are performed on these components.

Scalars, Vectors, Matrices, and Tensors

Scalars, vectors, matrices, and tensors are the actual components of any deep learning model. Having a fundamental understanding of how to utilize these components, as well as the operations that can be performed on them, is key to understanding how ANNs operate. Scalars, vectors, and matrices are examples of the general entity known as a tensor, so the term tensors may be used throughout this chapter but may refer to any component. Scalars, vectors, and matrices refer to tensors with a specific number...

Introduction to Keras

Building ANNs involves creating layers of nodes. Each node can be thought of as a tensor of weights that are learned in the training process. Once the ANN has been fitted to the data, a prediction is made by multiplying the input data by the weight matrices layer by layer, applying any other linear transformation when needed, such as activation functions, until the final output layer is reached. The size of each weight tensor is determined by the size of the shape of the input nodes and the shape of the output nodes. For example, in a single-layer ANN, the size of our single hidden layer can be thought of as follows:

Figure 2.16: Solving the dimensions of the hidden layer of a single-layer ANN

If the input matrix of features has n rows, or observations, and m columns, or features, and we want our predicted target to have n rows (one for each observation) and one column (the predicted value), we can determine the size of our hidden layer...

Summary

In this chapter, we covered the various types of linear algebra components and operations that pertain to machine learning. These components include scalars, vectors, matrices, and tensors. The operations that were applied to these tensors included addition, transposition, and multiplication—all of which are fundamental for understanding the underlying mathematics of ANNs.

We also learned some of the basics of the Keras package, including the mathematics that occurs at each node. We replicated the model from the previous chapter, in which we built a logistic regression model to predict the same target from the online shopping purchasing intention dataset. However, in this chapter, we used the Keras library to create the model using an ANN instead of the scikit-learn logistic regression model. We achieved a similar level of accuracy using ANNs.

The upcoming chapters of this book will use the same concepts we learned about in this chapter; however, we will continue...

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Key benefits

  • 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

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. The Deep Learning with Keras 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 practical 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 your own 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

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Product Details


Publication date : Jul 29, 2020
Length 496 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800562967
Category :
Concepts :

Table of Contents

11 Chapters
Preface Chevron down icon Chevron up icon
1. Introduction to Machine Learning with Keras Chevron down icon Chevron up icon
2. Machine Learning versus Deep Learning Chevron down icon Chevron up icon
3. Deep Learning with Keras Chevron down icon Chevron up icon
4. Evaluating Your Model with Cross-Validation Using Keras Wrappers Chevron down icon Chevron up icon
5. Improving Model Accuracy Chevron down icon Chevron up icon
6. Model Evaluation Chevron down icon Chevron up icon
7. Computer Vision with Convolutional Neural Networks Chevron down icon Chevron up icon
8. Transfer Learning and Pre-Trained Models Chevron down icon Chevron up icon
9. Sequential Modeling with Recurrent Neural Networks Chevron down icon Chevron up icon
Appendix Chevron down icon Chevron up icon

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