read ebook (pdf) 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:
Volume II: Computer Vision

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Why this book?Are you looking for a book where you can learn about deep learning and PyTorch
without having to spend hours deciphering cryptic text and code? A technical book that&#8217also
easy and enjoyable to read?This is it!How is this book different?First, this book presents an easyto-follow,
structured, incremental, and from-first-principles approach to learning PyTorch.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&#8217llbe 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&#8217llhave a
thorough understanding of the concepts and tools necessary to start developing, training, and finetuning
computer-vision models using PyTorch.If your goal is to learn about deep learning models
for computer vision, and you&#8217realready comfortable training simple models in PyTorch, the
second volume is the right one for you.What&#8217InsideDeep models, activation functions, and
feature spacesTorchvision, datasets, models, and transformsConvolutional neural networks,
dropout, and learning rate schedulersTransfer learning and fine-tuning popular models (ResNet,
Inception, etc.)