Image classification using neural networks with Python MNIST dataset This project utilizes the The Modified National Institute of Standards and Technology (MNIST) dataset which comprises of 60 thousand training observations and 10 thousand test observations of handwritten digits.
Due to the complexity of imaging data, connectionist network models or neural networks have been gaining significant interest in recent years. Python machine learning packages sklearn and keras have been especially useful for specifying neural network architectures.
What? In the world of web and desktop applications, frameworks for different software languages such as Rails for Ruby and Electron for JavaScript are popular. Analogous to Rails, Go uses the Wails framework and offers the benefits of Go’s reported processing speed. Wails also uses the native rendering engine unlike Electron which uses an embedded browser. Furthermore, Wails integrates user interface (UI) frameworks such as React, Vue and Svelte.
To demo Go’s frontend capabilities, two projects are implemented here using Wails and Svelte:
What? Concurrency or concurrent processing is breaking up a single process into independent components to coordinate compute. It is based on the idea of CSP or Communicating Sequential Processes (Brookes, Hoare, and Roscoe 1984). In contrast to parallel processing, the paradigm here is shifted towards communication and splitting of independent processes instead of sharing memory. To communicate between different processes, Go utilizes goroutines, which are similar to channels as described in CSP.
