Alternate title: Elements of Modern AI Models Covers: Transformers, LLM Fine-tuning, Generative AI, and Deep RL
This repository contains material for a deep learning course (IDS 576) at UIC.
Audience: Enthusiastic business analysts with intermediate Python programming experience.
Click the "Open in Colab" badge on any notebook to run it directly in your browser with free GPU access.
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Clone the repository
git clone https://github.com/thejat/dl-notebooks.git cd dl-notebooks -
Set up Miniconda (Linux/macOS)
./miniconda_setup.sh
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Install dependencies
pip install -r requirements.txt
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Launch Jupyter
jupyter notebook
| Module | Topic | Materials |
|---|---|---|
| M01 | Review & ML Pipeline | Slides |
| M02 | Feed-Forward Networks | Slides |
| M03 | CNNs & Transfer Learning | Slides, LoRA, PyTorch |
| M04 | Text & NLP | Slides |
| M05 | Recurrent Networks & Attention | Slides, Attention |
| M06 | Transformers | Slides, LLM Fundamentals |
| M07 | Unsupervised Learning (VAEs, Diffusion & GANs) | Slides |
| M08 | Repeated Decision Making & Bandits | Slides |
| M09 | Reinforcement Learning | Slides |
| M10 | Deep Reinforcement Learning | Slides |
| Category | Notebooks |
|---|---|
| Python & PyTorch Basics | Python Review, PyTorch Prelims |
| Feed-Forward Networks | Linear Classifier, FFN Classifier |
| CNNs | CNN Classifier, t-SNE MNIST |
| NLP & RNNs | RNN Sentiment, LSTM Sentiment |
| LLM Fine-tuning | Fine-tuning & LoRA |
| Reinforcement Learning | Q-Learning CliffWorld |
- Python 3.10+ (intermediate level)
- Libraries: PyTorch, NumPy, Pandas, Matplotlib, Jupyter
- Prior coursework: Data mining (IDS 572) and machine learning (IDS 575) or equivalent
Please see the Syllabus for:
- Full course schedule and dates
- Assignment details and deadlines
- Project requirements (Project.md)
- Grading breakdown
- Textbook recommendations
Additional resources:
- Lecture Goals - Learning objectives and external resources for each lecture
Copyright © 2021-2026 Theja Tulabandhula. All Rights Reserved.
See LICENSE for details.