What you’ll get: a rigorous, reproducible pipeline that goes from data acquisition (open sources), through exploratory data analysis and geospatial feature engineering (distance to beaches, train stations, CBD), to modeling with XGBoost / LightGBM, evaluation, and interpretation (SHAP). All code examples are ready to copy into a Jupyter notebook. TL;
Summary: This tutorial walks through building a Retrieval-Augmented Generation (RAG) system for a finance use case — an Investor Q&A assistant that answers questions using a company's SEC filings, earnings call transcripts, and analyst research. You'll get architecture guidance, production considerations, and runnable sample code
Precision, Recall, F1-Score for classification; MAE, RMSE for regression; Perplexity for LLMs.
Master prompt optimization for generative AI, chatbots, and search. Learn techniques, tools, and workflows that improve LLM output accuracy, relevance, and efficiency, essential skills for AI developers and prompt engineers in Sydney and beyond.
Transformers revolutionized deep learning by replacing recurrence and convolutions with attention. This guide explains key components, Q/K/V attention, multi-head mechanisms, and shows how these ideas power modern models like GPT, BERT, and ViT.
Summary: A step-by-step tutorial on building a custom AI assistant using OpenAI’s GPT API, tailored to specific workflows (like a code reviewer, writing assistant, or customer support bot). You’ll Learn: * How to set up an OpenAI API key * How to structure prompts and responses * Using function calling and
In this tutorial, we will build an agentic workflow using a large language model (LLM), integrating tools and memory to create an autonomous agent capable of reasoning, acting, and remembering past interactions. We'll use Python and OpenAI’s API as an example. 1. Setup First, install the required