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Computational Intelligence and Deep Learning 🧠🤖

Welcome to the official repository for the course Computational Intelligence and Deep Learning.

This repository serves as a comprehensive resource hub, providing you with essential slides, materials, and collaborative code developed during lectures

What's Inside? 📂

  1. Slides: Access the presentation slides used in class to reinforce key concepts and insights.
  2. Materials: Find supplementary materials that complement the course content, helping you delve deeper into specific topics.
  3. Collaborative Code: Explore the code developed collectively during lectures. This collaborative effort ensures a hands-on understanding of the theoretical concepts discussed.

How to Navigate 🗺️

Feel free to navigate through the repository to locate the specific resources you need. Exploring the repository is quite straightforward. Each lecture has its dedicated folder, housing a README file and associated materials for your convenience.

Lectures 🎓

  1. Lecture 1: Brief introduction to Stochastic Gradient Descent (SGD) and deep dive into the fundamentals of computational graphs, covering both Forward Mode and Reverse Mode Differentiation.
  2. Lecture 2: Deep dive into Tensor Algebra and PyTorch: from understanding tensor operations to mastering PyTorch datasets, dataloaders, modules, and training.
  3. Lecture 3: Introduction about Convolutional Neural Networks (CNN) and ResNet
  4. Recap 1: Recap lecture about PyTorch, MLP, CNNs and ResNet.
  5. Lecture 4: Recurrent Neural Networks, LSTM and GRU.
  6. Lecture 5: Autoencoders and Variational Autoencoders.
  7. Lecture 6: Vector Quantized Variational Autoencoders.
  8. Lecture 7: Generative Adversarial Networks (GANs).
  9. Lecture 8: Advanced Topics in Deep Learning and Computer Vision.

Contribution Guidelines 🤝

Your active participation is encouraged! If you have suggestions, improvements, or additional resources to share, please open an Issue or a PR on GitHub.

License 🔑

This content is released under the Creative Commons License (CC BY-NC 4.0), allowing you to share and adapt the materials for non-commercial purposes, with appropriate attribution. Refer to the specific license file for more details.

You are free to:

  • Share: copy and redistribute the material in any medium or format
  • Adapt: remix, transform, and build upon the material

Under the following terms:

  • Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial: You may not use the material for commercial purposes.