This is the index for the Deep Learning projects section — twelve end-to-end builds, from a first image classifier through fine-tuning an LLM, each with a full problem statement, architecture, working code, and deployment notes. This is where everything from the rest of this hub comes together.
The Twelve Projects
| Project | Domain | Level |
|---|---|---|
| Image Classification | Computer Vision | Beginner–Intermediate |
| Object Detection | Computer Vision | Intermediate–Advanced |
| Image Segmentation | Computer Vision | Advanced |
| Sentiment Analysis (LSTM) | NLP | Beginner–Intermediate |
| Text Generation | NLP | Intermediate |
| Chatbot | NLP / LLM | Intermediate |
| GAN Image Generation | Generative | Advanced |
| Diffusion Image Generation | Generative | Advanced |
| Transformer From Scratch | NLP / Architecture | Advanced |
| Fine-Tuning an LLM | LLM | Advanced |
| Multimodal (Image Captioning) | Multimodal | Advanced |
| Deployment | MLOps | Intermediate–Advanced |
How Every Project Is Structured
- Problem Statement — what's being built and why, with a clearly defined success criterion (directly applying DL Problem Definition).
- Dataset — what data is used and where to get it.
- Architecture & Approach — the model design and key decisions.
- Step-by-Step Build — real, working code for the core pipeline.
- Expected Results — what a reasonable outcome looks like, so you know if your own run is on track.
- Key Learnings & Extensions — what the project actually teaches, and how to push it further.
How to Get the Most Out of These Projects
- Don't just run the code — modify it. Change the architecture, try a different dataset, break something on purpose and fix it. Running someone else's working code teaches far less than getting your own version working after it breaks.
- Apply the project lifecycle discipline from earlier in this hub. Explore the data first, establish a simple baseline before the full model, and do error analysis on your results — treat each project as a small real project, not just a script to execute.
- Start smaller than you think you need to. Get a minimal version working end-to-end (even on a tiny subset of data) before scaling up — this catches pipeline bugs early and cheaply, exactly as covered in Model Training.
- Push at least one project through to actual deployment — the Deployment project shows how, and doing this at least once closes the loop between "a model that works in a notebook" and "a model someone can actually use."
Key Takeaways
- These twelve projects span the full range of what this Deep Learning hub has covered — CNNs, sequence models, Transformers, generative models, LLMs, and deployment.
- Each project is a genuine, complete build, not a toy snippet — treat it as practice for a real project, applying the full lifecycle discipline from the DL Project Development category.
- This is the natural final stop in the curriculum — from first-principles math, through architectures and modern LLMs, through interview and practice prep, to building and shipping real things.
