Related Neural Networks Links
Learn Handson Projects Neural Networks Tutorial, validate concepts with Handson Projects Neural Networks MCQ Questions, and prepare interviews through Handson Projects Neural Networks Interview Questions and Answers.
Neural Networks
Projects
Portfolio
Experiments
Hands-On NN Projects
Reading tutorials is not the same as training models end-to-end. These project ladders go from quick wins to portfolio pieces. For each project, aim for: a clean train/val split, logged metrics, a short README (data, commands, results), and fixed random seeds when comparing ideas.
baseline first version data ablations
Beginner
- MNIST / Fashion-MNIST — MLP then a small CNN; compare accuracy and training time.
- CIFAR-10 — data augmentation, residual-ish block or pretrained backbone.
- Tabular classification — embeddings + MLP vs gradient boosting (XGBoost/LightGBM) as a sanity baseline.
Intermediate
- Transfer learning — classify a small custom image set with a frozen then fine-tuned ResNet or ViT.
- Text sentiment — fine-tune a small transformer or use TF-IDF + linear model as baseline.
- Time series — LSTM or 1D CNN for forecasting; report MAE/RMSE on a rolling validation.
Ship a minimal demo: Gradio, Streamlit, or a notebook with clear “Run all†instructions—recruiters can reproduce your work.
Summary
- Start with baselines; improve with architecture, data, and regularization—not random tweaks.
- Document setup and results; treat each repo as a mini research log.
- Next: practice exercises and quizzes to test understanding.
Reinforce concepts with practice exercises in the next page.