Data science agents should inspect datasets, design workflows, run code, and return verifiable answers, not just autocomplete Pandas code. DSGym, introduced by researchers from Stanford University, Together AI, Duke University, and Harvard University, is a framework that evaluates and trains…
Computer vision is an area of artificial intelligence that gives computer systems the ability to analyze, interpret, and understand visual data, namely images and videos. Source link
In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit graph structure. We show how we can organize knowledge in a tree-like hierarchy that mirrors how…
Suppose you’ve built your machine learning model, run the experiments, and stared at the results wondering what went wrong. Source link
I have been building a payment platform using vibe coding, and I do not have a frontend background. Source link
This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator. Source…
If you’ve trained a machine learning model, a common question comes up: “How do we actually use it?” This is where many machine learning practitioners get stuck. Source link
Editor’s note: This article is a part of our series on visualizing the foundations of machine learning. Source link
Large language models like LLaMA, Mistral, and Qwen have billions of parameters that demand a lot of memory and compute power. Source link
@tool def sql_investigate(query: str) -> dict: try: df = con.execute(query).df() head = df.head(30) return { “rows”: int(len(df)), “columns”: list(df.columns), “preview”: head.to_dict(orient=”records”) } except Exception as e: return {“error”: str(e)} @tool def log_pattern_scan(window_start_iso: str, window_end_iso: str, top_k: int = 8) ->…
