Tag: #tutorial25 results
A comprehensive workshop covering the full stack of LLM development, from math intuition and transformer architecture to GPU coding and reinforcement learning. The content is designed for those who prefer learning through code and practical examples over heavy theoretical prerequisites.
OpenAI has launched Images 2.0, hailed as the new king of image generation. The article offers hands-on testing with 10 practical tips to fully utilize it. It warns that the real risks go beyond improved drawing capabilities.
Developer implemented GPT-2, Llama 3.2-3B, and DeepSeek architectures from scratch in PyTorch over a year. Open-source code available on GitHub; detailed book on Leanpub covers explanations, derivations, and diagrams. Aimed at production-level LLM understanding.
OpenAI Blog offers step-by-step instructions to begin using Codex. Covers setting up projects, creating threads, and finishing first tasks.
Tutorial enables running Qwen3.5-397B-A13B with vLLM on 8x AMD R9700 GPUs using MXFP4 quantization. Achieves 30 t/s single request, up to 100 t/s batched. Includes Dockerfile patches and launch script for high throughput.
Tutorial teaches building an LLM from scratch trained on Mary Shelley's Frankenstein text. Includes in-depth guide on Substack and runnable Kaggle notebook on GitHub. Ideal for hands-on ML learning.
Poor questioning leads to unsatisfying generative AI responses. The article explains prompting techniques to get actionable business proposals. It provides 8 ready-to-use prompt examples to make AI a reliable partner.
Learn to leverage ChatGPT for gathering sources and analyzing information. It enables creation of structured insights backed by citations for reliable research outputs.
This guide explains how to build custom GPTs for automating workflows. It covers maintaining consistent outputs and creating purpose-built AI assistants tailored to specific needs.
A blog post documents a fun exploration of GANs basics and implements DCGAN to generate human faces. It covers core architecture and intuitions behind GANs. Full post linked for readers.