Applied Deep Learning - by Umberto Michelucci (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>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. </p><p>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. </p><p><i>Applied Deep Learning </i>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). </p><p><b>What You Will Learn</b><br></p><p></p><ul><li>Implement advanced techniques in the right way in Python and TensorFlow<br></li><li>Debug and optimize advanced methods (such as dropout and regularization)<br></li><li>Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)<br></li><li>Set up a machine learning project focused on deep learning on a complex dataset<br></li></ul><p></p><p><b>Who This Book Is For</b><br></p><p>Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming. <br></p><p><br></p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>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. </p><p>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. </p><p><i>Applied Deep Learning </i>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). </p><p>You will: <br></p><p></p><ul><li>Implement advanced techniques in the right way in Python and TensorFlow<br></li><li>Debug and optimize advanced methods (such as dropout and regularization)<br></li><li>Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)<br></li><li>Set up a machine learning project focused on deep learning on a complex dataset</li></ul><p></p><p/><br></br><p><b> About the Author </b></p></br></br>Umberto is currently the head of Innovation in BI & Analytics at a leading health insurance company in Switzerland, where he leads several strategic initiatives that deal with AI, new technologies and machine learning. He worked as data scientist and lead modeller in several big projects in healthcare and has extensive hands-on experience in programming and designing algorithms. Before that he managed projects in BI and DWH enabling data driven solutions to be implemented in complicated productive environments. He worked extensively with neural networks the last two years and applied deep learning to several problems linked to insurance and client behaviour (like customer churning). He presented his results on deep learning at international conferences and internally gained a reputation for his huge experience with Python and deep learning. <br>
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