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Advanced Machine Learning with Python

Advanced Machine Learning with Python

3.1 (7)
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Advanced Machine Learning with Python

Advanced Machine Learning with Python

3.1 (7)

Overview of this book

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.
Table of Contents (12 chapters)
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10
A. Chapter Code Requirements
11
Index

Summary

In this chapter, we covered a lot of ground. We began by introducing a new kind of neural network, the convnet. We explored the theory and architecture of a convnet in the most ubiquitous form and also by discussing some state-of the-art network design principles that have been developing as recently as mid-2015 in organizations such as Google and Baidu. We built an understanding of the topology and also of how the network operates.

Following this, we began to work with the convnet itself, applying it to the CIFAR-10 dataset. We used modular convnet code to create a functional architecture that reached a reasonable level of accuracy in classifying 10-class image data. While we're definitely still at some distance from human levels of accuracy, we're gradually closing the gap! Chapter 8, Ensemble Methods will pick up from what you learned here, taking these techniques and their application to the next level.

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83
Tech Concepts
36
Programming languages
73
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Advanced Machine Learning with Python
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