Prof.ai to founder.ai

When Covid-19 struck in 2020, Sashikumaar Ganeshan at the Indian Institute of Science, Bangalore built a model to predict the spread of the contagion, marking his deep immersion into AI technologies.
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
GitHub

Neural Network from Scratch

Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...
Medical Xpress

AI model predicts neural network degeneration patterns in ALS progression

New research from the University of St Andrews, the University of Copenhagen and Drexel University has developed AI computational models that predict the degeneration of neural networks in amyotrophic ...
Frontiers

A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation

In learning goal-directed behavior, state representation is important for adapting to the environment and achieving goals. A predictive state representation called successive representation (SR) has ...
IEEE

A Switched System Model for Exponential Stability and Dissipativity of Delayed Neural Networks

Abstract: This article investigates the problems of exponential stability and dissipativity for neural networks with time-varying delays. To capture more information on the delay and its derivative in ...
IEEE

Deep Operator Neural Network Model Predictive Control

Abstract: In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets) (Lu et al. 2021). These neural networks are capable of ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
Hosted on MSN

Dropout In Neural Networks — Prevent Overfitting Like A Pro (With Python)

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...
Hosted on MSN

Build A Deep Neural Network From Scratch In Python — No Tensorflow!

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
C&EN

Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Understanding and manipulating the conformational behavior of a molecule in different ...