Many industry experts consider unsupervised learning the next
frontier in artificial intelligence, one that may hold the key to
general artificial intelligence. Since the majority of the world's
data is unlabeled, conventional supervised learning cannot be
applied. Unsupervised learning, on the other hand, can be applied
to unlabeled datasets to discover meaningful patterns buried deep
in the data, patterns that may be near impossible for humans to
uncover. 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
General
Imprint: |
O'Reilly Media
|
Country of origin: |
United States |
Release date: |
March 2019 |
Authors: |
Ankur A. Patel
|
Dimensions: |
250 x 150 x 15mm (L x W x T) |
Format: |
Paperback
|
Pages: |
400 |
ISBN-13: |
978-1-4920-3564-0 |
Categories: |
Books
|
LSN: |
1-4920-3564-5 |
Barcode: |
9781492035640 |
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