Deep Learning with PyTorch Step-by-Step: A Beginner's Guide - Volume II: Computer Vision. Deep Learning with PyTorch Step-by-Step: A Beginner's Guide, #2
Par :Formats :
- Compatible avec une lecture sur My Vivlio (smartphone, tablette, ordinateur)
- Compatible avec une lecture sur liseuses Vivlio
- Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony

Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement
- FormatePub
- ISBN8230653059
- EAN9798230653059
- Date de parution18/02/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently Published
Résumé
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. What will I learn?In this second volume of the series, you'll be introduced to deeper models and activation functions, convolutional neural networks, initialization schemes, learning rate schedulers, transfer learning, and more.
By the time you finish this book, you'll have a thorough understanding of the concepts and tools necessary to start developing, training, and fine-tuning computer-vision models using PyTorch. If your goal is to learn about deep learning models for computer vision, and you're already comfortable training simple models in PyTorch, the second volume is the right one for you. What's Inside Deep models, activation functions, and feature spaces Torchvision, datasets, models, and transforms Convolutional neural networks, dropout, and learning rate schedulers Transfer learning and fine-tuning popular models (ResNet, Inception, etc.)
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. What will I learn?In this second volume of the series, you'll be introduced to deeper models and activation functions, convolutional neural networks, initialization schemes, learning rate schedulers, transfer learning, and more.
By the time you finish this book, you'll have a thorough understanding of the concepts and tools necessary to start developing, training, and fine-tuning computer-vision models using PyTorch. If your goal is to learn about deep learning models for computer vision, and you're already comfortable training simple models in PyTorch, the second volume is the right one for you. What's Inside Deep models, activation functions, and feature spaces Torchvision, datasets, models, and transforms Convolutional neural networks, dropout, and learning rate schedulers Transfer learning and fine-tuning popular models (ResNet, Inception, etc.)