Fundamentals of deep learning : designing next-generation machine intelligence algorithms / Nithin Buduma...[et al].

Contributor(s):

Buduma, Nithin [author]

Material type: Text

TextLanguage: English Publication details: Mumbai : Shroff, 2022.Edition: 2nd EdDescription: xiii, 372 pISBN:

9789355420121

Subject(s):

Machine learning

DDC classification:

23 006.31

Summary: "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."--

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Holdings
Item type	Home library	Collection	Shelving location	Call number	Materials specified	Status	Date due	Barcode
Print	OPJGU Sonepat- Campus	General Books	Main Library	006.31 FU- (Browse shelf(Opens below))		Checked out	07/01/2024	150292

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Previous	006.30222 AP-D Dream machine : AI and the real world /	006.309 ME-G Genius makers : the mavericks who brought AI to Google, Facebook and the world /	006.31 FO-D Data smart using data science to transform information into insight	006.31 FU- Fundamentals of deep learning : designing next-generation machine intelligence algorithms /	006.31 MU-D Deep learning	006.31 RA-M Machine learning using R	006.31 SR-M Machine learning	Next

"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."--

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