slipstream-finetune
Finetune LLMs to speak Slipstream natively - complete guide with GLM-4-9B
llm-basics
LLM architecture, tokenization, transformers, and inference optimization. Use for understanding and working with language models.
prompt-engineering
Prompt design, optimization, few-shot learning, and chain of thought techniques for LLM applications.
deep-learning
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
ml-fundamentals
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
supervised-learning
Build production-ready classification and regression models with hyperparameter tuning
few-shot-prompting
Example-based prompting techniques for in-context learning
Machine Learning
Python machine learning with scikit-learn, PyTorch, and TensorFlow
ai-ml-technologies
Master AI, machine learning, LLMs, prompt engineering, and blockchain development. Use when building AI applications, working with LLMs, or developing smart contracts.
feature-stores
Master feature stores - Feast, data validation, versioning, online/offline serving
dspy-finetune-bootstrap
Fine-tune LLM weights using DSPy's BootstrapFinetune optimizer
dspy-rag-pipeline
Build and optimize RAG pipelines with ColBERTv2 retrieval in DSPy
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