Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems Aurélien Géron.
Publication details: Mumbai: Shroff Publishers & Distributors, 2019.Edition: First editionDescription: xx, 545 pages : illustrations ; 24 cmISBN:- 9781491962299
- 9789352135219 (Pbk)
- Machine learning
- Artificial intelligence
- COMPUTERS / Computer Vision & Pattern Recognition
- COMPUTERS / Data Processing
- COMPUTERS / Intelligence (AI) & Semantics
- COMPUTERS / Natural Language Processing
- COMPUTERS / Neural Networks
- Artificial intelligence
- Machine learning
- Python 3.0
- Automatische Klassifikation
- Maschinelles Lernen
- Künstliche Intelligenz
- 006.31 G319H 23
- Q325.5 .G47 2017
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | |
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Central Library, IISER Bhopal Reference Section | Reference | 006.31 G319H (Browse shelf(Opens below)) | Not For Loan | Reserve | 10155 |
Browsing Central Library, IISER Bhopal shelves, Shelving location: Reference Section, Collection: Reference Close shelf browser (Hides shelf browser)
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006.30954 G35F Futures of artificial intelligence : | 006.31 D368M Mathematics for machine learning | 006.31 D368M Mathematics for machine learning | 006.31 G319H Hands-on machine learning with Scikit-Learn and TensorFlow : | 006.31 G549D Deep learning : | 006.31 G61D Deep learning | 006.31 M695M Machine Learning |
Includes index.
"Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started" --
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