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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

By : Anthony So , William So , Nagy
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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

5 (1)
By: Anthony So , William So , Nagy

Overview of this book

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.
Table of Contents (8 chapters)
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Preface

Regularization

As with any machine learning algorithm, neural networks can face the problem of overfitting when they learn patterns that are only relevant to the training set. In such a case, the model will not be able to generalize the unseen data.

Luckily, there are multiple techniques that can help reduce the risk of overfitting:

  • L1 regularization, which adds a penalty parameter (absolute value of the weights) to the loss function
  • L2 regularization, which adds a penalty parameter (squared value of the weights) to the loss function
  • Early stopping, which stops the training if the error for the validation set increases while the error decreases for the training set
  • Dropout, which will randomly remove some neurons during training

All these techniques can be added at each layer of a neural network we create. We will be looking at this in the next exercise.

Exercise 6.04: Predicting Boston House Prices with Regularization

In this exercise, you will...

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