Deep Learning with PyTorch
This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
Offered by
Difficulty Level
Intermediate
Completion Time
8h44m
Language
English
About Book
Who Is This Book For?
This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
Deep Learning with PyTorch
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 8h44m total length
Getting Started with Pytorch for Deep Learning
Mathematical building blocks of Neural Networks
Getting Started with Neural Networks
Fundamentals of Machine Learning
Deep Learning for Computer Vision
Natural Language Processing for PyTorch
Advanced neural network architectures
Generative networks
Conclusion
Related Resources
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