REPOSITORY OVERVIEWLive repository statistics
★ 4Stars
⑂ 2Forks
◯ 0Open issues
◉ 4Watchers
34/100
OPENREPOHUB HEALTH SIGNALLimited signals
A transparent discovery signal based on current public GitHub metadata.
Recent activity35% weight
10 Community adoption25% weight
11 Maintenance state20% weight
100 License clarity10% weight
0 Project information10% weight
75 This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
README preview
Neural-Networks
Author: Rohan Singh
This repository contains the source code for various types of Neural Networks from scratch, as well as their applications. The types of Neural Networks in this repository range from simple neural networks (single layer, hidden layer) to more advanced multi-layer neural networks. The source code in this directory is Python and no external libraries libraries like Scikit-learn, Keras etc. are used.
Simple Neural Networks
This Directory contains the source code for some of the basic types of Neural Networks, these include:
- Single Layer (Multi-input -> Single-output): This directory contains source code for a general single layer n-to-1 neural network.
- Single Layer (Multi-input -> Multi-output): This directory contains source code for a general single layer n-to-m neural network.
- Hidden Layer (Multi-input -> Single-output): This directory contains source code for a general non-linearity (hidden layer) n-to-1 neural network.
- Hidden Layer (Multi-input -> Multi-output): This directory contains source code for a general non-linearity (hidden layer) n-to-m neural network.
Perceptrons
This directory uses the hidden layer code from the Simple Neural Networks directory to make different types of Perceptrons, which include:
- Single Layer Perceptron: This directory contains the source code for a single binary perceptron neural network.
- Multi-Layer Perceptron: This directory contains the source code for a multi-layer activation perceptron neural network.
Cancer Diagnosis Neural Network
This directory contains source code for a simple neural network that can be used to differentiate between malignant and benign tumors in patients. This is achieved by using a Multi-Layer Neural Network with Sigmoid and Threshold non-linearities. This directory contains:
- Multi-Layer Neural Network: This directory contains the multi-layer neural network code that I will use.
- Diagnosis Code: This directory contains the code for the diagnosis using the afforementioned Neural Network.
Contact
Please feel free to reach out to me using:
rohan.b.singh54@gmail.com
rxs1182@case.edu
ALGORITHMICALLY RELATEDSimilar Open-Source Projects
Selected from shared topics, language and repository description—not editorial ratings.
This repository aims at containing all the code employed at LIVIA to segment medical images. Mainly, our research focuses on bringind the expertise in deep learning and optimization techniques to the medical image analysis domain.
37/100 healthActive repository
PythonNo license#convolutional-networks#convolutional-neural-networks#deep-learning#deep-neural-networks
⑂ 19 forks◯ 1 issuesUpdated Jun 8, 2025
This repository contains the implementation of evaluation metrics for recommendation systems. We have compared similarity, candidate generation, rating, ranking metrics performance on 5 different datasets - MovieLens 100k, MovieLens 1m, MovieLens 10m, Amazon Electronics Dataset and Amazon Movies and TV Dataset.
45/100 healthActive repository
PythonNo license#deep-learning#deep-neural-networks#evaluation-framework#evaluation-metrics
⑂ 14 forks◯ 0 issuesUpdated Nov 30, 2025