How fmata17/ml_lab works
This is a machine-learning study repository, not a packaged application: notebooks explore datasets, compare modeling approaches, and investigate specialized topics, with small shared Python utilities. The README describes a repeatable exploration-to-modeling-to-evaluation workflow, while the tree contains varied notebooks rather than the uniform per-dataset layout it describes. Edges reflect that documented workflow; notebook-specific connections are omitted where source was not sampled.
Main entry points: README.md Data exploration · iris Iris studies · mnist MNIST studies · data_exploration.ipynb Titanic exploration
Components 19
Experiment workflow
- Data exploration
README.md - Preprocessing and splits
README.md - Model experiments
README.md - Evaluation and analysis
README.md
Shared utilities
- EDA helpers
shared/eda_tools.py - Dataset utilities
shared/utils.py
Dataset studies
- Iris studies
notebooks/iris - MNIST studies
notebooks/mnist - Titanic exploration
notebooks/titanic/data_exploration.ipynb - Face recognition
notebooks/celebA_imageNet
Specialized studies
- Music generation
mit_dl/lab1_music_generation.ipynb - Debiasing and VAE
mit_dl/lab2 - LLM fine-tuning
mit_dl/lab3_LLM_Finetuning_Solution.ipynb - Transformer attention
- Continual learning
research/E1/CL_SGD_Split-MNIST.ipynb - CUDA learning
nvidia_dl_institute/An_Even_Easier_Introduction_to_CUDA.ipynb - Neural networks
nvidia_dl_institute/BuildingABrain.ipynb - Linear algebra demo
notebooks/linear_algebra/image_detector.py
Other components
- Researcher
Connections 16
- Researcher → Data exploration: studies data
README.md - Data exploration → Preprocessing and splits: prepares data
README.md - Preprocessing and splits → Model experiments: feeds models
README.md - Model experiments → Evaluation and analysis: evaluates results
README.md - Researcher → Iris studies: explores and models
- Researcher → MNIST studies: explores and models
- Researcher → Titanic exploration: explores data
- Researcher → Face recognition: studies recognition
- Researcher → Music generation: experiments with generation
- Researcher → Debiasing and VAE: studies debiasing
- Researcher → LLM fine-tuning: fine-tunes models
- Researcher → Transformer attention: studies attention
- Researcher → Continual learning: studies continual learning
- Researcher → CUDA learning: learns CUDA
- Researcher → Neural networks: studies neural networks
- Researcher → Linear algebra demo: runs demo