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)
15
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16
Index

Training on large datasets with online learning

So far, we have trained our model on no more than 300,000 samples. If we go beyond this figure, memory might be overloaded since it holds too much data, and the program will crash. In this section, we will explore how to train on a large-scale dataset with online learning.

SGD evolves from gradient descent by sequentially updating the model with individual training samples one at a time, instead of the complete training set at once. We can scale up SGD further with online learning techniques. In online learning, new data for training is available in sequential order or in real time, as opposed to all at once in an offline learning environment. A relatively small chunk of data is loaded and preprocessed for training at a time, which releases the memory used to hold the entire large dataset. Besides better computational feasibility, online learning is also used because of its adaptability to cases where...