000			01930 a2200229 4500
005			20251008171220.0
008			251008b |||||||| |||| 00| 0 eng d
020			_a9789355420121
082			_a006.3 _bBudF
100			_aBuduma,Nithin
245			_aFundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms _cNithin Buduma...[et al.]
250			_a2nd ed.
260			_bSPD : _aMumbai , _c©2022.
300			_axiii,372p.
520			_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			_aNeural Networks
650			_aMachine Learning
700			_aLocascio, Nicholas
700			_aNikhil, Buduma
700			_aJoe, Papa
942			_cBK
999			_c6596 _d6596