000			01568nam a22002297a 4500
003			PE-AndUNAJMA
005			20231207155029.0
007			ta
008			230427b xxu |||||||| |||| 00| 0 eng d
020			_a978-1-78712-593-3
040			_aUNAJMA _bspa _cepis
043			_aR1
082			_a006.4 _bR24
100			_aRaschka, Sebastian /Mirjalili, & Vahid _98308
245			_aPython machine learning : _bMachine learning and deep learning with python, sckit-learn, and tensorflow / _cRaschka, Sebastian
250			_a2a ed.
260			_aColumbia, USA _bPackt publishing _c2017
300			_a721 _c17 cm
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 sets - 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 -- Embedding a Machine Learning Model into a Web Application -- Predicting Continuous Target Variables with Regression Analysis -- Implementing a Multilayer Artificial Neural Network from Scratch -- Parallelizing Neural Network Training with TensorFlow -- Going Deeper - The Mechanics of TensorFlow -- Classifying Images with Deep Convolutionnal Neural Networks -- Modeling Seqquential Data Using Recurrent Neural Networks
700			_aMirjalili, & Vahid _99333
942			_2ddc _cLIB
999			_c13223 _d13203