Comparative Study of JavaScript and Python for Image Classification using TensorFlow

This research project offers a comparative study of JavaScript (TensorFlow.js for Node.js) and Python (TensorFlow) for image classification tasks using the MNIST dataset (a set of 70,000 handwritten digits). The study aims to address the lack of academic research directly comparing these two approaches and provide insights into the feasibility and practicality of using JavaScript for machine learning development.

Research Questions

The research seeks to answer the following questions:

1. How does the performance of JavaScript and Python compare in terms of training time, accuracy, and inference time?
2. What are the strengths and limitations of each language in the context of machine learning development?

Additionally, the developer experience is analyzed, considering factors like ease of implementation, code readability, and debugging.

Methodology

1. Implement a Convolutional Neural Network (CNN) architecture in both JavaScript and Python.
2. Train the models on the MNIST dataset.
3. Evaluate the performance of the models based on metrics such as training time, accuracy, and inference time.
4. Analyze the developer experience, considering factors like ease of implementation, code readability, and debugging.