## Microstructure Classification using Neural Networks – Streamlit App

In this post, I demonstrate a Streamlit app that I have created that can be used to classify microstructures of different types. I…

## Handwritten digit recognition using CrysX-NN neural network trained on MNIST_Plus

In the last post, I showcased an app based on a neural network, trained on the MNIST dataset using the CrysX-NN library. The…

## Handwritten digit recognition using CrysX – Neural Network library and Streamlit Web App

Recently, I wrote a blog post on creating a Streamlit app for recognizing handwritten digits using a convolutional neural network model with the…

## Digit recognition App (Streamlit) trained on the MNIST_Plus dataset using PyTorch CNN model

In my previous blog post, I shared with you a streamlit app that recognizes handwritten digits entered by the user through mouse/touch input.…

## Streamlit App for Digit recognition trained on the MNIST dataset using PyTorch

Recently, I learned how to program deep and convolutional neural networks using various frameworks like PyTorch and TensorFlow-Keras. Here is an application of…

## Mean Squared Error loss function and its gradient (derivative) for a batch of inputs [Python Code]

In this post, I show you how to implement the Mean Squared Error (MSE) Loss/Cost function as well as its derivative for Neural…

## Efficient implementation of Tanh activation function and its Derivative (gradient) in Python

The mathematical definition of the Tanh activation function is and its derivative is defined as The Tanh function and its derivative for a…

## Efficient implementation of Softplus activation function and its Derivative (gradient) in Python

The mathematical definition of the Softplus activation function is with the derivative defined as , which is actually the Sigmoid function. We have…
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