000 02286 a2200277 4500
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020 _a9789355420121
082 _a006.31
_bBUD
100 _aBuduma, Nithin
245 _aFundamentals of deep learning: designing next generation machine intelligence algorithms
250 _a2nd
260 _aBeijing:
_bO'Reilly Media,
_c2022.
300 _axiii, 372p.:
_bill; pbk:
_c23cm.
504 _aIncludes bibliographical references and index.
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 https://www.oreilly.com/library/view/fundamentals-of-deep/9781492082170/
650 _aMachine learning
650 _aDeep learning (Machine learning)
650 _aMachine learning--Mathematical models
650 _aNeural networks (Computer science)--Models
650 _aArtificial intelligence
650 _aPyTorch
650 _aBuilding intelligent machines
700 _aBuduma, Nikhil
_eCo-author
700 _aPapa, Joe
_eCo-author
942 _cTD
_2ddc
999 _c59064
_d59064