GitHub topics: deepneuralnetworks

Repositories

ultralytics/magellan

Earth observation software powered by Machine Learning (ML). Viewable in Google Maps and WebGL Earth.

Language: MATLAB - Size: 58.7 MB - Last synced at: 28 days ago - Pushed at: 28 days ago - Stars: 2 - Forks: 2

muhannad-iz-a-tech-nerd/Youtube-

A free, detailed course about AI and its essentials and Web dev. (Full-Stack)

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NavuluriBalaji/Design-and-Evaluation-of-a-Deep-Learning-Model-for-Counting-Objects-in-Satellite-Images

This study proposes the design and evaluation of a deep learning model using YOLOv8, an advanced object detection algorithm, for object detection and counting in satellite images

Language: Jupyter Notebook - Size: 1.27 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

GitHub-HongweiZhang/prediction-flow

Deep-Learning based CTR models implemented by PyTorch

Language: Python - Size: 787 KB - Last synced at: 12 months ago - Pushed at: about 4 years ago - Stars: 247 - Forks: 51

iambankaratharva/Weapon-Detection

Detection of people and weapons from video footage or images. The detection time was 30ms - 35ms per frame with Darknet-YOLOv4. A custom dataset was used for the training of the object detection model and data integration was performed using OIDv4 ToolKit.

Language: Jupyter Notebook - Size: 10.1 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0

cdalsania/Credit_Card_Fraud_Detection

This project researched the credit card transaction dataset and tried various machine learning classification models on the dataset to determine the best model that would flag suspicious activity more accurately.

Language: Jupyter Notebook - Size: 21.9 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

drewlr/pytorch_intro

Language: Jupyter Notebook - Size: 7.54 MB - Last synced at: almost 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

Sanky18/Crack-Detection-Model

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

Language: Jupyter Notebook - Size: 1.48 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

duncangrubbs/lyrics-to-artist

Finding a Artists Style

Language: Jupyter Notebook - Size: 3.97 MB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 7 - Forks: 1

Arcadianlee/Deep-Learning-Design-Photonic-Crystals

Modeling and designing Photonic Crystal Nanocavities via Deep Learning

Language: Python - Size: 13.2 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 5 - Forks: 7

IoBT-VISTEC/EEGANet

EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)

Language: Jupyter Notebook - Size: 120 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 2

AdityaTheDev/ConvolutionalNeuralNetwork-To-Classify-DogVsCat

Convolutional Neural Network to Classify Dogs and Cat. I built a ImageClassifier which classifies and tells you whether its a Dog image or a Cat image. I built a convolutional network which consists of Three Convolution layer and Three MaxPooling layer. Each Convolutional layer has filters, kernel size. Maxpooling layer has stride and pooling size. Then this Convolutional layer Connects to DeepNeuralNetwork. DNN has three hidden layer and output layer having Sigmoid Activation function. I trained this model for 31 epochs and achieved an accuracy of around 85%. I found this massive image dataset online which has 10,028 images(Ten Thousand and Twenty Eight). My model Predicted accurately during the testing phase. I even tested my model using my neighbor dog's pic and it predicted accurately.

Language: Jupyter Notebook - Size: 838 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

chhayac/Gender-Classification-Using-Images

Gender Prediction from Social Media profile pictures using Deep Learning

Size: 3.91 KB - Last synced at: about 1 month ago - Pushed at: over 6 years ago - Stars: 3 - Forks: 0

SanyaGera/Breast-Cancer-Classification-with-Deep-Learning

This is a deep learning model used to identify whether a particular type of breast cancer is malignant or benign. It takes 30 features like area, texture, perimeter etc. in input and based on that features does the required classification. The project involves making deep neural network and training and testing the model with the help of data available from load breast cancer dataset.

Language: Jupyter Notebook - Size: 4.88 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0