Browsing: Guide

Image by Author   # Introduction  Running a top-performing AI model locally no longer requires a high-end workstation or expensive cloud setup. With lightweight tools and smaller…

In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a…

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab.…

‘Skill’ is the latest buzzword in agentic AI workflows, and you will know this for sure if you use any of the AI coding platforms today.…

Pandas DataFrames provide powerful tools for selecting and indexing data efficiently. The two most commonly used indexers are .loc and .iloc. The .loc method selects data…

In this tutorial, we take a deep dive into nanobot, the ultra-lightweight personal AI agent framework from HKUDS that packs full agent capabilities into roughly 4,000…

Transformers power modern NLP systems, replacing earlier RNN and LSTM approaches. Their ability to process all words in parallel enables efficient and scalable language modeling, forming…

One of the core challenges of data science is drawing meaningful causal conclusions from observational data. In many such cases, the goal is to estimate the…

import os, sys, subprocess, importlib, pathlib SENTINEL = “/tmp/diffrax_colab_ready_v3” def _run(cmd): subprocess.check_call(cmd) def _need_install(): try: import numpy import jax import diffrax import equinox import optax import…

Moving AI agents from prototypes to production surfaces a challenge that traditional testing is unable to address. Agents are flexible, adaptive, and context-aware by design, but…