Info8010-Deep-Learning

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info8010-deep-learning is an open-source collection of lecture materials for the INFO8010 Deep Learning course at ULiège. It provides comprehensive resources for students and enthusiasts interested in deep learning.

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Open Source

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Yes

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Technical

About

What is info8010-deep-learning?

info8010-deep-learning is a GitHub repository offering a comprehensive set of lecture materials for the INFO8010 Deep Learning course at ULiège. This resource includes lecture PDFs, associated code examples (e.g., for polynomial regression, multi-layer perceptrons, automatic differentiation in PyTorch, convolutional neural networks, attention, transformers, GPT, graph neural networks, uncertainty, auto-encoders, and diffusion models), and homework assignments designed to familiarize users with the PyTorch library. It also provides a course syllabus, project guidelines, and archived lectures from previous editions, making it an invaluable educational tool for anyone looking to learn or teach deep learning concepts.

Best used for

Ideal for professors who need to teach deep learning courses, students who need to learn deep learning concepts, and individuals looking to practice PyTorch skills. Especially valuable for those seeking structured educational content with practical code examples.

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learn deep learning

teach deep learning

access course materials

practice PyTorch

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Key features

Lecture PDFs
Code examples
Homework assignments
Course syllabus
Project guidelines
Archived lectures

Target Audience

professorteacherstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What topics are covered in the INFO8010 Deep Learning course materials?

The course materials cover a wide range of deep learning topics including fundamentals of machine learning, multi-layer perceptrons, automatic differentiation, training neural networks, convolutional neural networks, computer vision, attention and transformers, GPT, graph neural networks, uncertainty, auto-encoders, and diffusion models.

Are there practical coding exercises included with the lectures?

Yes, each lecture is accompanied by practical code examples, often in PyTorch, covering concepts like polynomial regression, spiral classification, differentiable fluid simulation, ResNet to ConvNeXt, UNet, Vision Transformer, GPT from scratch, and variational auto-encoders.

Are the homework assignments mandatory for learning?

The homework assignments are optional but highly recommended for familiarizing users with the PyTorch library. They cover tensor operations, autograd, neural networks, datasets, dataloaders, GPU usage, and training convolutional neural networks.

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