Getting Started with Hugging Face Transformers
Learn how to use Hugging Face Transformers library for NLP tasks including text classification, named entity recognition, and text generation.
📚 Guides & Tutorials
In-depth tutorials and comprehensive guides for AI/ML practitioners.
PyTorch Fundamentals for Deep Learning
Master PyTorch basics: tensors, autograd, neural networks, and training loops. Essential knowledge for any ML engineer.
Fine-tuning LLMs with LoRA and QLoRA
Efficiently fine-tune large language models using LoRA and QLoRA. Train 7B+ parameter models on consumer GPUs with 4-bit quantization.
Running LLMs Locally with Ollama
Set up and run open-source LLMs on your local machine using Ollama. Supports Llama, Mistral, Phi, and more with simple CLI commands.
Building RAG Applications with LangChain
Create Retrieval-Augmented Generation systems to query your own documents. Combine vector databases with LLMs for accurate, grounded responses.
Prompt Engineering Techniques
Master prompt engineering for better LLM outputs. Learn zero-shot, few-shot, chain-of-thought, and advanced prompting strategies.
Deploying ML Models with FastAPI
Build production-ready ML APIs with FastAPI. Learn model serving, async endpoints, input validation, and Docker deployment.
GPU Cloud Providers Comparison
Compare GPU cloud providers for ML training: Vast.ai, RunPod, Lambda Labs, and major clouds. Find the best price-performance for your workload.
Vector Databases Explained
Understand vector databases for AI applications. Compare Pinecone, Weaviate, Chroma, Milvus, and Qdrant for semantic search and RAG.
MLOps with MLflow
Track experiments, manage models, and deploy ML pipelines with MLflow. The open-source standard for ML lifecycle management.
Running Stable Diffusion Locally
Set up Stable Diffusion on your local machine for image generation. Covers Automatic1111, ComfyUI, and the diffusers library.
Data Labeling Best Practices
Create high-quality training datasets with proper labeling workflows. Tools, techniques, and quality assurance for ML data annotation.
LLM Evaluation Metrics
Measure LLM performance with the right metrics. BLEU, ROUGE, perplexity, and human evaluation frameworks for text generation.
Python Environment Management for ML
Manage Python environments for ML projects with conda, venv, and uv. Avoid dependency conflicts and ensure reproducibility.
Transformer Architecture Explained
Understand the Transformer architecture that powers GPT, BERT, and modern LLMs. Attention mechanisms, positional encoding, and key components.
Model Quantization Guide
Reduce model size and speed up inference with quantization. INT8, INT4, GPTQ, AWQ, and GGUF formats explained.
Git Best Practices for ML Projects
Version control for machine learning: handling large files, tracking experiments, and collaborating on ML codebases.
Text Embeddings Guide
Generate and use text embeddings for semantic search, clustering, and classification. Compare embedding models and best practices.
Containerizing ML Applications with Docker
Package ML models and applications in Docker containers for reproducible deployments. GPU support, multi-stage builds, and best practices.
AI Safety and Alignment Basics
Understand AI safety concepts: alignment, RLHF, constitutional AI, and responsible deployment practices for LLMs.