Python

Learn how to reliably extract structured data from LLMs. Covers native JSON modes, Pydantic integration, function calling, and handling edge cases.

Learn how to structure Python ML projects for production. Covers project layout, dependency management, configuration, testing, and CI/CD practices.

Learn how to build robust, scalable APIs for machine learning models using FastAPI. Covers async patterns, streaming, background tasks, and production deployment.

Take your RAG systems to the next level with advanced techniques like query expansion, hybrid search, reranking, and sophisticated chunking strategies.

A comprehensive guide to understanding, building, and deploying AI agents. Learn about agent architectures, tool use, memory systems, and production considerations.

Explore multimodal AI models like LLaVA, GPT-4V, and Qwen-VL that understand both images and text, with practical code examples.

A practical guide to LLM quantization techniques for running large models on consumer hardware with minimal quality loss.

Learn how to run open-source LLMs locally using Ollama and vLLM for privacy, cost savings, and low-latency inference.

A practical comparison of LangChain and LlamaIndex for building LLM-powered applications with code examples for common use cases.

Master the core techniques of prompt engineering—from zero-shot to chain-of-thought—to get consistent, high-quality results from any LLM.

A comprehensive guide to building Retrieval Augmented Generation (RAG) systems that combine the power of LLMs with your own knowledge base.

Understand how vector databases work under the hood, when to use them, and how to choose between Pinecone, Weaviate, ChromaDB, and Qdrant for your application.

Learn how Named Entity Recognition works, when to use different approaches, and how to train custom models for domain-specific entities.

A guide to text embeddings from Word2Vec to Sentence Transformers—how they work, when to use each type, and practical implementation patterns.

A comprehensive introduction to LLM fine-tuning covering key concepts, different approaches, and guidance on choosing the right method for your use case.

Understand how LoRA enables fine-tuning of billion-parameter models on consumer GPUs by learning low-rank adaptations instead of updating all weights.

Comprehensive guide to OpenCV for computer vision applications, covering image processing, feature detection, object tracking, and real-time video analysis with practical examples.

Understanding ArcFace loss - the breakthrough technique that revolutionized face recognition by teaching networks to create better feature boundaries through angular margins.

Quick setup to get everything installed on your local machine for tensorflow and pytorch

I go through my experience attending the Melbourne Datathon 2016 and placing 4th.