000			01039nam a22001577a 4500
008			240329b |||||||| |||| 00| 0 eng d
020			_a9781718500723
082			_a006.31 _bGLA
100			_aGlassner, Andrew _9106454
245			_aDeep Learning: A Visual Approach
260			_bNo Starch Press _c2021 _aUSA
300			_a736p
504			_aPart I: Foundational Ideas An Overview of Machine Learning Essential Statistics Measuring Performance Bayes’ Rule Curves and Surfaces Information Theory Part II: Basic Machine Learning Classification Training and Testing Overfitting and Underfitting Data Preparation Classifiers Ensembles Part III: Deep Learning Basics Neural Networks Backpropagation Optimizers PART IV: Beyond the Basics Convolutional Neural Networks Convnets in Practice Autoencoders Recurrent Neural Networks Attention and Transformers Reinforcement Learning Generative Adversarial Networks Creative Applications
600			_aComputer Engineering _9104391
942			_2ddc _cLB
999			_c148442 _d148442