Training the Same Neural Network with Different Optimizers

Gradient Thoughts — Fri, 02 Jan 2026 05:40:17 +0000

I've trained the same neural network by changing only the optimizers and got some really interesting results. Same Model, Same Data, Varying Optimizers: What Actually Changes?
Check it out in my latest blog below:

https://medium.com/p/498c4a37748c

Hosting your Machine Learning Model with Flask and Docker

Gradient Thoughts — Wed, 22 Oct 2025 05:03:39 +0000

Developing and training a machine learning model is only half the journey. The real impact comes when your model steps out of the local Python environment and starts interacting with the real world. By deploying it as an API, you enable seamless integration with web applications, dashboards, or even mobile apps — transforming your standalone model into a practical, usable solution.

Check out my step-by-step guide to make your ML model fully functional and an integral part of the application development workflow.

Medium Blog:

medium.com

P.S.
You are highly encouraged to check out the GitHub repository as well, containing all the required files and scripts.

Pranav-Bhatlapenumarthi / Deploy_ML

Designed and deployed a production-ready Flask API hosting a trained Machine Learning model on Render. Containerized the application using Docker for environment consistency and easy deployment.

Deploy_ML

A web-based Machine Learning API built with Flask that serves real-time predictions from a trained model. The API is containerised with Docker and deployed on Render, making it accessible for integration with frontend applications or other services.

For a step-by-step description of the application, check out this hands-on demonstration: https://medium.com/@hello.gradientthoughts/building-and-hosting-a-machine-learning-model-with-flask-and-docker-1cd4f89cf256

Features

Serve ML predictions: Host a trained model and provide predictions via a RESTful API.
JSON-based endpoints: Accepts feature inputs in JSON format.
Error handling: Returns informative error messages for invalid requests.
CORS enabled: Supports frontend integration from other domains.
Dockerized: Ensures environment consistency and easy deployment.

Project Structure

Deploy_ML/
│
├── app/
│   ├── main.py         # Flask application
│   ├── model.py        # Model loading and prediction
│
├── models/
│   └── model.joblib    # Trained ML model
│
├── src/
│   └── main_model.py   # Script for data preprocessing and model training
|
├── f1_dnf.csv          # Dataset to train

…

View on GitHub