Public course repository for DATA 5610-6610: Deep Learning, owned and maintained by Professor Pedram Jahangiry at Utah State University.
Fall 2026 covers Modules 1–7. Modules 8 and 9 are not taught this semester.
-
Create a GitHub account and install GitHub Desktop, or use Git from the command line.
-
Clone this repository:
git clone https://github.com/PJalgotrader/Deep_Learning-USU.git
-
Find the lecture slides and course notebooks in
Lectures and codes/. -
Pull regularly so your local copy stays current:
git pull
See the Fall 2026 course schedule for the detailed sequence of topics and assignments.
| Path | Contents |
|---|---|
Lectures and codes/ |
Module slides, notebooks, papers, and supporting examples |
Platforms and tools/ |
Google Colab, PyCaret, uv (student quick start, cheat sheet, ten-second test) and development-tool resources |
data/ |
Course datasets used by selected notebooks and examples |
images/ |
Images used by this README and other repository materials |
pyproject.toml + uv.lock |
The local uv environment (Python 3.13) for the PyCaret and scikit-learn notebooks |
environment.yml |
The same environment for conda users (dl_pycaret) |
scripts/ |
check_environment.py, which confirms that the local environment works |
One rule: if a notebook trains a neural network (Keras 3 / TensorFlow: Modules 4 to 7), run it on Google Colab with a GPU (Runtime > Change runtime type > GPU). Keras and TensorFlow are preinstalled there; nothing to set up. Everything else (Module 3, the Module 6 PyCaret forecasting notebook, and the demos in Platforms and tools/PyCaret/) runs on Colab or on your own computer, in one local environment that you build with uv (recommended) or conda. Same three options as the Machine Learning and Deep Forecasting courses, same commands.
| Notebooks | Where |
|---|---|
| Modules 4 to 7 (NN, CNN, RNN/LSTM, Transformers) | Google Colab, GPU runtime |
| Module 3 (ML review, scikit-learn + PyCaret) and the PyCaret demos | Colab, or your computer (uv or conda) |
The PyCaret notebooks use PyCaret 3.5.0 from the pycaret-core package (the old pycaret package does not run on Python 3.12 or newer, including Colab's). Their first cell installs it on Colab and does nothing on your own machine.
Open the notebook with its Colab badge and run it from the top, in a fresh runtime (Runtime > Disconnect and delete runtime if you already imported PyCaret in that session).
uv downloads Python 3.13, creates a .venv inside this repository and installs the exact versions recorded in uv.lock. It does not touch any Python or Anaconda you already have. New to uv? Start with Platforms and tools/uv/ (quick start, conda-to-uv cheat sheet, ten-second test).
-
Install uv (instructions), then reopen the terminal and check
uv --version.Windows (PowerShell):
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
macOS / Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh -
Clone into a normal local folder (not inside Google Drive or OneDrive) and build the environment:
git clone https://github.com/PJalgotrader/Deep_Learning-USU.git cd Deep_Learning-USU uv sync -
Check it:
uv run python scripts/check_environment.py
The last line must be
Your course environment is ready. -
Start Jupyter:
uv run jupyter lab
VS Code: register the environment once as a Jupyter kernel, then pick it with Select Kernel > Jupyter Kernel > Python 3.13 (Deep Learning):
uv run python -m ipykernel install --user --name deep-learning --display-name "Python 3.13 (Deep Learning)"git clone https://github.com/PJalgotrader/Deep_Learning-USU.git
cd Deep_Learning-USU
conda env create -f environment.yml
conda activate dl_pycaret
python scripts/check_environment.py
jupyter labIn VS Code, dl_pycaret shows up under Select Kernel > Python Environments.
The full guide and troubleshooting table are in the PyCaret setup guide.
| Module | Topic |
|---|---|
| 1 | Introduction to Deep Learning |
| 2 | Setting Up the Deep Learning Environment |
| 3 | Machine Learning Review: Fundamentals and Models |
| 4 | Deep Neural Networks: NN and DNN |
| 5 | Deep Computer Vision: CNN, R-CNN, YOLO, and FCN |
| 6 | Deep Sequence Modeling: RNN and LSTM |
| 7 | Transformers: Attention Is All You Need |
Fall 2026 scope: Modules 8 and 9 are not part of this semester’s course delivery.
- Course materials may be revised during the semester. Pull updates regularly and follow Canvas for official announcements, assignments, deadlines, and grades.
- Do not commit passwords, API keys, access tokens, student records, or other private information.
- Do not publish homework solutions, answer keys, or restricted team work unless the instructor explicitly permits it.
- Review notebook cells and outputs before committing or sharing them publicly.
Pedram Jahangiry, CFA is a Professional Practice Assistant Professor of Data Analytics and Information Systems in the Jon M. Huntsman School of Business at Utah State University. Before joining the Huntsman School in 2018, he was a research associate in BlackRock’s Financial Modeling Group in New York. His teaching and applied research focus on machine learning, deep learning, and time-series forecasting.
Pedram is also a project mentor with the Analytics Solutions Center, which provides experiential learning opportunities through analytics projects with organizational partners.
