A collection of exploratory notebooks covering topics in Large Language Models (LLMs) and Machine Learning. This repo serves as a personal lab for experimentation, prototyping, and documentation of learning in the AI/ML space.
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📚 Large Language Models
- Prompt engineering
- Fine-tuning & adapters (LoRA, PEFT)
- Retrieval-Augmented Generation (RAG)
- Tokenization & embeddings
- Evaluation techniques
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🤖 Machine Learning
- Supervised learning (classification, regression)
- Unsupervised learning (clustering, dimensionality reduction)
- Model evaluation (ROC, AUC, precision-recall)
- Feature engineering & selection
- Pipelines with Scikit-Learn and others
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📊 Experimentation
- Benchmarking model performance
- Data preprocessing and visualization
Clone the repo:
git clone https://github.com/edcalderin/llm-ml-experiments.git
cd llm-ml-notebooksLinkedIn: https://www.linkedin.com/in/erick-calderin-5bb6963b/
e-mail: edcm.erick@gmail.com
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I regularly share insights, tutorials, and reflections on tech, AI, and more. Your feedback and thoughts are always welcome!