Hands-on unsupervised learning using python : how to build applied machine learning solutions from unlabeled data
Material type: TextPublication details: O′Reilly, Mumbai : ©2023Description: xix, 337 p. : ill. ; 24 cmISBN:- 9789352138128
- 006.31 PAT-H
Contents:
1.Unsupervised Learning in the Machine Learning Ecosystem 2.End-to-End Machine Learning Project
3.Dimensionality Reduction 4.Anomaly Detection 5.Clustering 6.Group Segmentation 7.Autoencoders 8.Hands-On Autoencoder 9.Semisupervised Learning 10.Recommender Systems Using Restricted Boltzmann Machines 11.Feature Detection Using Deep Belief Networks
12.Generative Adversarial Networks
13.Time Series Clustering
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | IIITD General Stacks | Computer Science and Engineering | 006.31 PAT-H (Browse shelf(Opens below)) | Available | 012499 |
Total holds: 0
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006.31 MOR-A AI and machine learning for on-device development : | 006.310 NIE-E Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics | 006.31 PAT-D Deep learning : a practitioner's approach | 006.31 PAT-H Hands-on unsupervised learning using python : | 006.31 PRO-A Applied machine learning and AI for engineers : | 006.31 PRU-P Practicing trustworthy machine learning : | 006.31 RAH-M Machine learning using R |
1.Unsupervised Learning in the Machine Learning Ecosystem 2.End-to-End Machine Learning Project
3.Dimensionality Reduction 4.Anomaly Detection 5.Clustering 6.Group Segmentation 7.Autoencoders 8.Hands-On Autoencoder 9.Semisupervised Learning 10.Recommender Systems Using Restricted Boltzmann Machines 11.Feature Detection Using Deep Belief Networks
12.Generative Adversarial Networks
13.Time Series Clustering
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