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[null,"1.","Record Nr.",null,"UNINA9910919815903321"]
[null,null,"Autore",null,"Bhasin Harsh"]
[null,null,"Titolo",null,"Hands-on Deep Learning : A Guide to Deep Learning with Projects and Applications / / by Harsh Bhasin"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024"]
[null,null,"ISBN",null,"9798868810350"]
[{"$nulls":4},"9798868810343"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (373 pages)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Python (Computer program language)"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Python"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 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."]
[null,null,null,"Sommario/riassunto","This 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, "]
[{"$nulls":4},"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? ."]