• Home
  • Contact
Menu
  • Home
  • Contact

Category: neural networks

Image Segmentation: Brain MRI Segmentation

  • September 10, 2020June 30, 2021
  • by Niranjan B Subramanian

In this article we’ll see how to perform Brain tumor segmentation from MRI images. We’ll try different architectures which are popular for image segmentation problems. Let’s start by defining what our business problem is. Business Problem: […]

Continue readingMore Tag

Introduction to Neural Networks Part – 2

  • September 3, 2020June 27, 2021
  • by Niranjan B Subramanian

In the Part 1 of Introduction to Neural Networks we learned about one of the simplest artificial neuron called McCulloch-Pitts neuron and implemented it in python. The problem with M-P neuron is that there is actually […]

Continue readingMore Tag

Introduction to Neural Networks – Part 1

  • August 28, 2020June 27, 2021
  • by Niranjan B Subramanian

A neural network is an algorithm whose design was inspired by the functioning of the human brain. It tries to emulate the basic functions of the brain. Due to the intentional design of ANNs as conceptual […]

Continue readingMore Tag

Dropout for Regularization

  • June 10, 2019June 27, 2021
  • by Niranjan B Subramanian

INTRODUCTION: When we have deep neural networks, the biggest problem is overfitting. We can say a neural network overfits when it has excellent performance in the training data and has a poor performance in unseen data. […]

Continue readingMore Tag

Introduction to activation functions

  • May 27, 2019June 27, 2021
  • by Niranjan B Subramanian

Why do we need activation functions? We use activation function to introduce non-linearity into the neural network. Activation functions convert independent variables of near infinite range into simple probabilities between 0 and 1. Now the question […]

Continue readingMore Tag

Copyright © 2019 AI ASPIRANT | All Rights Reserved.