{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"DEV","title":"Neural Network Quantization: Shrinking Models Without Losing Accuracy","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/06c5e758\"></iframe>","width":"100%","height":180,"duration":465,"description":"Neural network quantization lets developers shrink bloated models for mobile, edge, and cloud deployment — without sacrificing meaningful accuracy. This episode breaks down how it works, which approach fits your use case, and where the real-world tradeoffs lie.","thumbnail_url":"https://img.transistorcdn.com/FjCd-OuusfvO3o_XEB1lBI9M3jCiMFpn2OICEsvCyrs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9kYzVl/MjVhMjFhZGZhOTg4/Zjc1YTFlMGNkZWE1/ZmVhMi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}