LEADER 00800nam0-22002891i-450- 001 990006316450403321 005 19980601 035 $a000631645 035 $aFED01000631645 035 $a(Aleph)000631645FED01 035 $a000631645 100 $a19980601d1970----km-y0itay50------ba 105 $a--------00-yy 200 1 $aQuaestiones vel distinctiones$fPietro De Bellapertica. 210 $aBologna$cForni$d1970 215 $as.p.$d24 cm 225 1 $aOpera iuridica rariora$v11 676 $a340.5 700 1$aBelleperche,$bPierre : de$f$0238030 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006316450403321 952 $aV MA 42 (11)$b91676$fFGBC 959 $aFGBC 996 $aQuaestiones vel distinctiones$9655762 997 $aUNINA DB $aGIU01 LEADER 04072nam 22005775 450 001 9910919815903321 005 20251113191236.0 010 $a9798868810350$b(electronic bk.) 010 $z9798868810343 024 7 $a10.1007/979-8-8688-1035-0 035 $a(MiAaPQ)EBC31862043 035 $a(Au-PeEL)EBL31862043 035 $a(CKB)37083962500041 035 $a(DE-He213)979-8-8688-1035-0 035 $a(CaSebORM)9798868810350 035 $a(OCoLC)1482310053 035 $a(OCoLC-P)1482310053 035 $a(EXLCZ)9937083962500041 100 $a20241227d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on Deep Learning $eA Guide to Deep Learning with Projects and Applications /$fby Harsh Bhasin 205 $a1st ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (373 pages) 311 08$aPrint version: Bhasin, Harsh Hands-On Deep Learning Berkeley, CA : Apress L. 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