LEADER 02428nam 22005773 450 001 9910958755703321 005 20251117113713.0 010 $a9783838260273 010 $a3838260279 035 $a(CKB)4100000008340147 035 $a(MiAaPQ)EBC5782371 035 $a(Au-PeEL)EBL5782371 035 $a(OCoLC)1104083836 035 $a(Exl-AI)5782371 035 $a(EXLCZ)994100000008340147 100 $a20210901d2014 uy 0 101 0 $ager 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aDie "Russische Partei" $eDie Bewegung der russischen Nationalisten in der UdSSR 1953-1985 205 $a1st ed. 210 1$aBerlin :$cIbidem Verlag,$d2014. 210 4$d©2014. 215 $a1 online resource (152 pages) 225 1 $aSoviet and Post-Soviet Politics and Society ;$vv.134 330 $aThe book by Matthias Blazek explores the lives and crimes of two notorious serial killers, Carl Großmann and Friedrich Schumann, in 1920s Germany. It delves into their backgrounds, criminal activities, and the societal context of post-World War I Germany. The author uses police reports, court documents, and media articles to paint a detailed picture of their heinous acts and the subsequent legal proceedings. The book examines the psychological and societal factors that may have contributed to their behavior, offering insights into the mindset of serial killers. 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P.,c2025 9798868810343 327 $aChapter 1: Revisiting Machine Learning -- Chapter 2: Introduction to Deep Learning -- Chapter 3: Neural Networks -- Chapter 4: Training Deep Networks -- Chapter 5: Hyperparameter Tuning -- Chapter 6: Convolutional Neural Networks: Part 1 -- Chapter 7: Convolutional Neural Networks : Part 2 -- Chapter 8: Transfer Learning -- Chapter 9: Recurrent Neural Networks -- Chapter 10: LSTM and GRU -- Chapter 11: Autoencoders -- Chapter 12: Introduction to Generative Models -- Appendices A-G. 330 $aThis book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will Learn What are deep neural networks? What is transfer learning, multi-task learning, and end-to-end learning? What are hyperparameters, bias, variance, and data division? What are CNN and RNN? . 606 $aArtificial intelligence 606 $aMachine learning 606 $aPython (Computer program language) 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aPython 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aPython. 676 $a006.3 700 $aBhasin$b Harsh$01778243 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910919815903321 996 $aHands-on Deep Learning$94306451 997 $aUNINA