03779nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153050001400166072001600180072002300196072001500219100007600234245012900310250001800439264005400457300004400511336002600555337002600581338003600607347002400643505049100667520180701158650002902965650002202994650004003016650002903056650002203085650001203107710003403119773002603153776003603179776003603215776003603251856004603287979-8-8688-1035-0DE-He21320251030114645.0cr nn 008mamaa241227s2024    xxu|    s    |||| 0|eng d  a97988688103507 a10.1007/979-8-8688-1035-02doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aBhasin, Harsh.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aHands-on Deep Learningh[electronic resource] :bA Guide to Deep Learning with Projects and Applications /cby Harsh Bhasin.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXX, 364 p. 191 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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? . 0aArtificial intelligence. 0aMachine learning. 0aPython (Computer program language).14aArtificial Intelligence.24aMachine Learning.24aPython.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886881034308iPrinted edition:z979886881036708iPrinted edition:z979886881361040uhttps://doi.org/10.1007/979-8-8688-1035-0