Ankur A Patel - Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
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Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
Set up and manage machine learning projects end-to-end
Build an anomaly detection system to catch credit card fraud
Clusters users into distinct and homogeneous groups
Perform semisupervised learning
Develop movie recommender systems using restricted Boltzmann machines
Generate synthetic images using generative adversarial networks
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by Ankur A. Patel
Copyright 2019 Human AI Collaboration, Inc. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- February 2019: First Edition
- 2019-02-21: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492035640 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Hands-On Unsupervised Learning Using Python, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaims all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions containedin this work is at your own risk. If any code samples or other technologythis work contains or describes is subject to open source licenses or theintellectual property rights of others, it is your responsibility toensure that your use thereof complies with such licenses and/or rights.
978-1-492-03564-0
[LSI]
Machine learning is a subfield of artificial intelligence (AI) in which computers learn from datausually to improve their performance on some narrowly defined taskwithout being explicitly programmed. The term machine learning was coined as early as 1959 (by Arthur Samuel, a legend in the field of AI), but there were few major commercial successes in machine learning during the twenty-first century. Instead, the field remained a niche research area for academics at universities.
Early on (in the 1960s) many in the AI community were too optimistic about its future. Researchers at the time, such as Herbert Simon and Marvin Minsky, claimed that AI would reach human-level intelligence within a matter of decades:
Machines will be capable, within twenty years, of doing any work a man can do.
Herbert Simon, 1965
From three to eight years, we will have a machine with the general intelligence of an average human being.
Marvin Minsky, 1970
Blinded by their optimism, researchers focused on so-called strong AI or general artificial intelligence (AGI) projects, attempting to build AI agents capable of problem solving, knowledge representation, learning and planning, natural language processing, perception, and motor control. This optimism helped attract significant funding into the nascent field from major players such as the Department of Defense, but the problems these researchers tackled were too ambitious and ultimately doomed to fail.
AI research rarely made the leap from academia to industry, and a series of so-called AI winters followed. In these AI winters (an analogy based on the nuclear winter during this Cold War era), interest in and funding for AI dwindled. Occasionally, hype cycles around AI occurred but had very little staying power. By the early 1990s, interest in and funding for AI had hit a trough.
AI has re-emerged with a vengeance over the past two decadesfirst as a purely academic area of interest and now as a full-blown field attracting the brightest minds at both universities and corporations.
Three critical developments are behind this resurgence: breakthroughs in machine learning algorithms, the availability of lots of data, and superfast computers.
First, instead of focusing on overly ambitious strong AI projects, researchers turned their attention to narrowly defined subproblems of strong AI, also known as weak AI or narrow AI. This focus on improving solutions for narrowly defined tasks led to algorithmic breakthroughs, which paved the way for successful commercial applications. Many of these algorithmsoften developed initially at universities or private research labswere quickly open-sourced, speeding up the adoption of these technologies by industry.
Second, data capture became a focus for most organizations, and the costs of storing data fell dramatically driven by advances in digital data storage. Thanks to the internet, lots of data also became widely and publicly available at a scale never before seen.
Third, computers became increasingly powerful and available over the cloud, allowing AI researchers to easily and cheaply scale their IT infrastructure as required without making huge upfront investments in hardware.
These shows a chart from Google Trends, indicating the growth in interest in machine learning over the past five years.
AI is now viewed as a breakthrough horizontal technology, akin to the advent of computers and smartphones, that will have a significant impact on every single industry over the next decade.
Successful commercial applications involving machine learning includebut are certainly not limited tooptical character recognition, email spam filtering, image classification , computer vision, speech recognition, machine translation, group segmentation and clustering, generation of synthetic data, anomaly detection, cybercrime prevention, credit card fraud detection, internet fraud detection, time series prediction, natural language processing, board game and video game playing, document classification, recommender systems, search, robotics, online advertising, sentiment analysis, DNA sequencing, financial market analysis, information retrieval, question answering, and healthcare decision making.
The milestones presented here helped bring AI from a mostly academic topic of conversation then to a mainstream staple in technology today.
1997: Deep Blue, an AI bot that had been in development since the mid-1980s, beats world chess champion Garry Kasparov in a highly publicized chess event.
2004: DARPA introduces the DARPA Grand Challenge, an annually held autonomous driving challenge held in the desert. In 2005, Stanford takes the top prize. In 2007, Carnegie Mellon University performs this feat in an urban setting. In 2009, Google builds a self-driving car. By 2015, many major technology giants, including Tesla, Alphabets Waymo, and Uber, have launched well-funded programs to build mainstream self-driving technology.
2006: Geoffrey Hinton of the University of Toronto introduces a fast learning algorithm to train neural networks with many layers, kicking off the deep learning revolution.
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