000			02019nam a22002417a 4500
005			20240814152500.0
008			231215b2022 |||||||| |||| 00| 0 eng d
020			_a9789355420121
082			_a005.1 BUD-N
100			_aBuduma, Nithin
245			_aFundamentals of deep learning : _bdesigning next-generation machine intelligence algorithms / _cNithin Buduma and others
250			_a2nd ed.
260			_aIndia _bShroff Publishers _c2022
300			_a372 p.
365			_aINR _b1650.00.
500			_aWe'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
650			_aArtificial intelligence
650			_aMachine learning
650			_aNeural networks (Computer science)
700			_aBuduma, Nikhil
700			_aPapa, Joe
700			_aLocascio, Nicholas
999			_c91360 _d91360