000			02031nam a2200241Ia 4500
003			NULRC
005			20250520103029.0
008			250520s9999 xx 000 0 und d
020			_a9781801819312
040			_cNULRC
050			_aQA 76.73.P98 .R37 2022
100			_aRaschka, Sebastian _eauthor
245		0	_aMachine Learning with PyTorch and Scikit-Learn : _bdevelop machine learning and deep learning models with Python / _cSebastian Raschka, Yuxi (Hayden) Liu and Vahid Mirjalili
260			_aBirmingham, UK : _bPackt Publishing, Limited, _cc2022
300			_axxix, 741 pages : _billustrations ; _c24 cm.
365			_bUSD47
504			_aIncludes index.
505			_aGiving Computers the Ability to Learn from Data -- Training Simple Machine Learning Algorithms for Classification -- A Tour of Machine Learning Classifiers Using Scikit-Learn -- Building Good Training Datasets - Data Preprocessing -- Compressing Data via Dimensionality Reduction -- Learning Best Practices for Model Evaluation and Hyperparameter Tuning -- Combining Different Models for Ensemble Learning -- Applying Machine Learning to Sentiment Analysis -- Predicting Continuous Target Variables with Regression Analysis -- Working with Unlabeled Data - Clustering Analysis -- Implementing a Multilayer Artificial Neural Network from Scratch.
520			_aMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.
650			_aDATA MANING
700			_aLiu, Yuxi (Hayden) ;Mirjalili, Vahid _eco-author;co-author
942			_2lcc _cBK
999			_c21795 _d21795