000			02942cam a2200409 i 4500
001			23049322
003			KWUST
005			20230824115443.0
008			230405t20222022caua b 001 0 eng d
010			_a 2023275149
015			_aGBC290577 _2bnb
020			_a9781492089926 _q(pbk.)
035			_a(OCoLC)on1328008121
040			_aUKMGB _beng _cKWUST _erda _dBDX _dOCLCF _dCDX _dNVC _dDLC
042			_alccopycat
050	0	0	_aQ325.5.B87 2022
100	1		_aButtfield-Addison, Paris, _eauthor.
245	1	0	_aPractical simulations for machine learning : _busing synthetic data for AI / _cParis and Mars Buttfield-Addison, Tim Nugent, and Jon Manning.
250			_aFirst edition.
264		1	_aSebastopol, CA : _bO'Reilly Media, Inc., _c2022.
300			_axv, 313 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
336			_astill image _bsti _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_aSimulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train traditional machine learning models. That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, with a focus on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. With this deeply practical book, you'll learn how to: Design an approach for solving ML and AI problems using simulations Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization (PPO) and soft actor-critic (SAO) Train ML models locally, concurrently, and in the cloud Use PyTorch, TensorFlow, the Unity ML-Agents and Perception Toolkits to enable ML tools to work with industry-standard game development tools.
650		0	_aMachine learning _xComputer simulation.
650		0	_aArtificial intelligence _xComputer simulation.
650		7	_aArtificial intelligence _xComputer simulation. _2fast _0(OCoLC)fst00817253
700	1		_aButtfield-Addison, Mars, _eauthor. _1https://isni.org/isni/0000000119044669
700	1		_aNugent, Tim, _eauthor. _1https://isni.org/isni/0000000434716760
700	1		_aManning, Jon, _eauthor.
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c2463 _d2463