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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Architecting a CNN for classification

Putting the three types of convolutional-related layers together, along with the fully connected layer(s), we can structure the CNN model for classification as follows:

Figure 12.6: CNN architecture

In this example, the input images are first fed into a convolutional layer (with ReLU activation) composed of a bunch of filters. The coefficients of the convolutional filters are trainable. A well-trained initial convolutional layer is able to derive good low-level representations of the input images, which will be critical to downstream convolutional layers if there are any, and also downstream classification tasks. Each resulting feature map is then downsampled by the pooling layer.

Next, the aggregated feature maps are fed into the second convolutional layer. Similarly, the second pooling layer reduces the size of the output feature maps. You can chain as many pairs of convolutional and pooling layers as you want. The second (or...