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Name: Federico Arenas
Type: User
Company: @materiom
Bio: AI Engineer. MSc in Artificial Intelligence, University of Edinburgh.
Location: London, England
Name: Federico Arenas
Type: User
Company: @materiom
Bio: AI Engineer. MSc in Artificial Intelligence, University of Edinburgh.
Location: London, England
This project focuses on using the Semantic Segmentation Deep Learning architecture DeepLAbV3+ on the Agriculture-VIsion dataset. We focus on improving the architecture's performance by solving the class imbalance problem present in the data.
The aim of this project is to explore the classification of images using a ResNet18 Convolutional Neural Network and a Bag of Visual Words (BoVW) method that uses Support Vector Machines (SVM) on a dataset that contains 1200 224 x 224 images of cats and dogs.
Step-by-step tutorial on how to create data with Blender for an object detection application, with ressources included.
Deformation-Aware 3D Model Embedding and Retrieval, ECCV 2020
In this paper we compare and evaluate two simple embedding models which can be constructed directly from a given co-occurrence matrix extracted from Twitter data; Positive Pointwise Mutual Information (PPMI), and Hellinger Principal Component Analysis (H-PCA). For each embedding model we consider three alternative metrics for word similarity: cosine, euclidean and manhattan distance.
In this repository I explore the effect of applying Residual Connections to a VGG CNN Architecture, as well as applying Batch Normalisation. The networks are tested on the CIFAR100 benchmark dataset.
This is a concise tutorial on applying PCA in the benchmark dataset Fashion MNIST. I analyse how the data compression process is done in visual information.
During this study we will explore the different regularisation methods that can be used to address the problem of overfitting in a given Neural Network architecture, using the balanced EMNIST dataset.
This is a brief tutorial on using Logistic Regression and Support Vector Machines for classification on the Fashion MNIST dataset.
Initial implementation of Datasets that Are Not Paper, published in NeurIPS 2022.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.