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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Picking the right activation functions

So far, we have used the ReLU and sigmoid activation functions in our implementations. You may wonder how to pick the right activation function for your neural networks. Detailed answers to when to choose a particular activation function are given next:

  • Linear: f(z) = z. You can interpret this as no activation function. We usually use it in the output layer in regression networks as we don't need any transformation to the outputs.
  • Sigmoid (logistic) transforms the output of a layer to a range between 0 and 1. You can interpret it as the probability of an output prediction. Therefore, we usually use it in the output layer in binary classification networks. Besides that, we sometimes use it in hidden layers. However, it should be noted that the sigmoid function is monotonic but its derivative is not. Hence, the neural network may get stuck at a suboptimal solution.
  • Softmax. As was mentioned in Chapter 5, Predicting Online...