Fundamentals of deep learning: designing next-generation machine intelligence algorithms
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But decipher...
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| Beteiligte Personen: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Beijing
O'Reilly
May 2022
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| Ausgabe: | Second edition |
| Schlagwörter: | |
| Links: | https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6987697 https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=6987697 |
| Zusammenfassung: | We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field. Learn the mathematics behind machine learning jargon Examine the foundations of machine learning and neural networks Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Explore methods in interpreting complex machine learning models Gain theoretical and practical knowledge on generative modeling Understand the fundamentals of reinforcement learning |
| Beschreibung: | First edition: 2017 |
| Umfang: | 1 Online-Ressource (xiii, 372 Seiten) |
| ISBN: | 9781492082156 |
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| spelling | Buduma, Nithin Verfasser aut Fundamentals of deep learning designing next-generation machine intelligence algorithms Nithin Buduma, Nikhil Buduma, and Joe Papa; with contributions by Nicholas Locascio Second edition Beijing O'Reilly May 2022 1 Online-Ressource (xiii, 372 Seiten) txt rdacontent c rdamedia cr rdacarrier First edition: 2017 We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field. Learn the mathematics behind machine learning jargon Examine the foundations of machine learning and neural networks Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Explore methods in interpreting complex machine learning models Gain theoretical and practical knowledge on generative modeling Understand the fundamentals of reinforcement learning Artificial intelligence Machine learning Neural networks (Computer science) Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Deep Learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Deep Learning (DE-588)1135597375 s DE-604 Buduma, Nikhil 1994- Verfasser (DE-588)1136495533 aut Papa, Joe Verfasser (DE-588)1250286557 aut Locascio, Nicholas (DE-588)1138387258 ctb Erscheint auch als Druck-Ausgabe 978-1-4920-8218-7 (DE-604)BV047583125 |
| spellingShingle | Buduma, Nithin Buduma, Nikhil 1994- Papa, Joe Fundamentals of deep learning designing next-generation machine intelligence algorithms Artificial intelligence Machine learning Neural networks (Computer science) Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4193754-5 |
| title | Fundamentals of deep learning designing next-generation machine intelligence algorithms |
| title_auth | Fundamentals of deep learning designing next-generation machine intelligence algorithms |
| title_exact_search | Fundamentals of deep learning designing next-generation machine intelligence algorithms |
| title_full | Fundamentals of deep learning designing next-generation machine intelligence algorithms Nithin Buduma, Nikhil Buduma, and Joe Papa; with contributions by Nicholas Locascio |
| title_fullStr | Fundamentals of deep learning designing next-generation machine intelligence algorithms Nithin Buduma, Nikhil Buduma, and Joe Papa; with contributions by Nicholas Locascio |
| title_full_unstemmed | Fundamentals of deep learning designing next-generation machine intelligence algorithms Nithin Buduma, Nikhil Buduma, and Joe Papa; with contributions by Nicholas Locascio |
| title_short | Fundamentals of deep learning |
| title_sort | fundamentals of deep learning designing next generation machine intelligence algorithms |
| title_sub | designing next-generation machine intelligence algorithms |
| topic | Artificial intelligence Machine learning Neural networks (Computer science) Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Artificial intelligence Machine learning Neural networks (Computer science) Deep Learning Maschinelles Lernen |
| work_keys_str_mv | AT budumanithin fundamentalsofdeeplearningdesigningnextgenerationmachineintelligencealgorithms AT budumanikhil fundamentalsofdeeplearningdesigningnextgenerationmachineintelligencealgorithms AT papajoe fundamentalsofdeeplearningdesigningnextgenerationmachineintelligencealgorithms AT locascionicholas fundamentalsofdeeplearningdesigningnextgenerationmachineintelligencealgorithms |