000			01948nam a22002177a 4500
999			_c3865 _d3865
005			20221122121349.0
008			221122b ||||| |||| 00| 0 eng d
020			_a9781484247211
082			_a006.31 _bMIC
100			_aMichelucci, Umberto _99086
245			_aApplied deep learning: _ba case-based approach to understanding deep neural networks
250			_a2nd
260			_bApress _aNew York _c2022
300			_axxi, 410 p.
365			_aINR _b1199.00
520			_aAbout this book Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).
650			_aMachine learning _92343
650			_aNeural networks (Computer science) _92344
650			_aPython (Computer program language) _910208
942			_2ddc _cBK