| 000 | 03277cam a2200289zu 4500 | ||
|---|---|---|---|
| 001 | 88880131 | ||
| 003 | FRCYB88880131 | ||
| 005 | 20250107232900.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250108s2020 fr | o|||||0|0|||eng d | ||
| 020 | _a9781839219535 | ||
| 035 | _aFRCYB88880131 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
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| 100 | 1 | _aArtasanchez, Alberto | |
| 245 | 0 | 1 |
_aArtificial Intelligence with Python _c['Artasanchez, Alberto', 'Joshi, Prateek'] |
| 264 | 1 |
_bPackt Publishing _c2020 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
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| 337 |
_bc _2rdamdedia |
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| 338 |
_bc _2rdacarrier |
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| 650 | 0 | _a | |
| 700 | 0 | _aArtasanchez, Alberto | |
| 700 | 0 | _aJoshi, Prateek | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88880131 _qtext/html _a |
| 520 | _aNew edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated and revised to Python 3.x, and TensorFlow 2 Seven new chapters that include AI on the cloud, RNNs and DL models, feature engineering, the machine learning data pipeline, and more New author with 25 years of experience in artificial intelligence across multiple industries and enterprise domains Book Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x and TensorFlow 2. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Understand RNNs and various DL models Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory. | ||
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