Welcome to the Deep Learning Workshop repository! This workshop provides a hands-on introduction to deep learning concepts and their implementation using PyTorch, offered as part of the Certificate Course in Artificial Intelligence.
The workshop is designed as a series of sessions, starting with a PyTorch introduction and progressing through advanced topics like CNNs and RNNs. Every session contains demonstration notebooks followed by Exercises (named as E*.ipynb).
- Pre-requisites: Basic Python programming and environment setup (Google Colab / Local).
- S0 - PyTorch Introduction and Linear Regression: Fundamentals of PyTorch, tensors, and basic workflow.
- S1 - Convolutional Neural Networks (CNN): Image classification and CNN foundations.
- S2 - RNN Sequence Modelling: Introduction to Recurrent Neural Networks and sequence data.
- S3 - RNN Sentiment Analysis: Applying RNNs to natural language processing and classification.
- S4 - RNN Encoder Decoder Models: Advanced sequence-to-sequence models using encoder-decoder architectures.
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Bourke, D. (2024). pytorch-deep-learning: Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course [GitHub repository]. GitHub. https://www.learnpytorch.io/
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Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Chau, D. H. (2020). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics (TVCG). IEEE. https://poloclub.github.io/cnn-explainer/
We have drawn inspiration, code snippets, and implementation details from different sources for the exercises and demonstrations included in this repository. The resources we have utilized are duly cited in the references section. We have made every effort to ensure that all contributions and sources are properly acknowledged. However, if we have inadvertently missed any source or reference, please feel free to bring it to our attention, and we will update the repository to reflect the necessary credits