29 articles
Series How OpenPose skeletal detection, Graphonomy human parsing, and custom body measurement algorithms work together to enable accurate virtual fitting — plus an exploration of PiFu for 2D-to-3D reconstruction.
Series What the GAN-based virtual try-on system achieved, where it failed (and why), the smartphone app design, and how diffusion models are changing everything for the next generation of META FIT.
Series How I tackled the universal 'what's for dinner' problem over a decade ago using classical data science — cleansing 20,000 recipes, 200,000 ingredient records, and nutritional data into a unified ML-ready dataset.
Series Using cosine similarity on nutritional vectors to find recipes that match a target meal's nutrition profile but offer completely different flavors — at both the recipe and menu level.
Series Years after building the original ML pipeline, LLMs changed everything — using the ChatGPT API to simplify elaborate recipes into weeknight-friendly meals, and reflecting on a decade-long journey from cosine similarity to LSTM to GPT.
Series Reframing meal planning as a text generation problem — using LSTM neural networks with temperature-controlled sampling to predict diverse, non-repetitive menus from historical meal sequences.
Series How an experiment where ChatGPT outperformed professional fund managers inspired me to build Senrigan, an AI stock price prediction service powered by a fine-tuned LLM.
Series My first attempt at fine-tuning ELYZA Llama-3-JP-8B on a local RTX 3060 GPU, and the hard lessons learned about VRAM limitations when training large language models.
Series A deep dive into LoRA, quantization, QLoRA, and SFTTrainer -- the key techniques that enable fine-tuning large language models on consumer hardware, validated through a Mount Fuji proof-of-concept experiment.