Book Image

Python Machine Learning by Example, - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example, - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Classifying clothing images with CNNs

As mentioned, the CNN model has two main components: the feature extractor composed of a set of convolutional and pooling layers, and the classifier backend similar to a regular neural network.

Architecting the CNN model

As the convolutional layer in Keras only takes in individual samples in three dimensions, we need to first reshape the data into four dimensions as follows:

>>> X_train = train_images.reshape((train_images.shape[0], 28, 28, 1))
>>> X_test = test_images.reshape((test_images.shape[0], 28, 28, 1))
>>> print(X_train.shape)
(60000, 28, 28, 1)

The first dimension is the number of samples, and the fourth dimension is the appended one representing the grayscale images.

Before we develop the CNN model, let's specify the random seed in TensorFlow for reproducibility:

>>> tf.random.set_seed(42)

We now import the necessary modules from Keras and initialize a Keras-based...