Show HN: I implemented a neural network in SQL (github.com)

A developer showcased an innovative implementation of a neural network using SQL, marking a significant step in combining database query languages with machine learning techniques. This project utilizes SQL to handle training and testing on the Fashion-MNIST dataset, processing data efficiently by leveraging SQL's capabilities for large-scale data manipulation. By constructing the neural network in SQL, the implementation showcases that complex machine learning models can be orchestrated within environments traditionally reserved for data storage and retrieval. This approach is particularly noteworthy for the AI/ML community as it challenges the conventional boundaries of where neural networks can be built and executed. Instead of relying solely on specialized languages like Python or frameworks like TensorFlow, the project allows for the modeling of neural networks directly in SQL, potentially simplifying integration in database-centric applications. Key technical details include the use of lazy data loading with Dask-backed datasets, optimization through the omission of zero-value pixels to speed up calculations, and SQL's ability to perform operations like joins and grouping to handle feedforward and backpropagation processes in the training of the neural network. This could lead to new methodologies in data-centric AI workflows and further exploration of SQL's capabilities in data science.