Introduction to the Deep Learning with PyTorch Project
The "Deep Learning with PyTorch" project relates to the book of the same name authored by Eli Stevens, Luca Antiga, and Thomas Viehmann. Published by Manning Publications, this resource is a practical guide aimed at those interested in exploring deep learning using the PyTorch library.
Overview of the Book
The book, "Deep Learning with PyTorch," aims to lay down the foundational concepts of deep learning while leveraging the powerful PyTorch library to bring these concepts to life through a project-based approach. Unlike traditional deep learning reference texts, this book serves as a guide that provides intuitive understanding and encourages further exploration of advanced materials independently. It explores core aspects of deep learning without exhausting the entire PyTorch API, choosing to concentrate on a strategic subset that is most beneficial for practitioners.
Target Audience
The book is particularly crafted for developers keen on embarking on or advancing their journey in deep learning. It caters to aspiring PyTorch users, including computer scientists, data scientists, software engineers, and students at undergraduate level or higher in related fields. The book does not strictly require prior deep learning expertise, although a background in basic imperative and object-oriented programming is expected. Prospective readers should be comfortable with Python and its environment, familiar with installing packages, and running scripts.
For those with experience in languages like C++, Java, JavaScript, or Ruby, the transition should be smooth, albeit some external learning will be required. Familiarity with NumPy and elementary linear algebra concepts, such as matrices, vectors, and dot products, will be advantageous to fully grasp the material.
About the Authors
The expertise behind this project is backed by three accomplished professionals:
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Eli Stevens: With extensive experience in Silicon Valley startups, Eli has traversed roles from software engineering in networking appliances to CTO in the radiation oncology software sphere. His current engagements involve machine learning within the autonomous vehicle sector.
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Luca Antiga: Luca's journey transitioned from biomedical engineering research in the 2000s to co-founding an AI engineering firm where he served as CTO. A PyTorch core contributor, Luca recently initiated a startup in the United States, focusing on data-defined software infrastructure.
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Thomas Viehmann: Based in Munich, Germany, Thomas is a machine learning specialist and consultant, alongside being a PyTorch core developer. With a mathematical background (PhD), Thomas combines theoretical knowledge with practical application in addressing computing challenges.
This trio brings a wealth of knowledge and practical experience to the table, making the "Deep Learning with PyTorch" book a valuable resource for practical deep learning knowledge using PyTorch.