Search Results for "self-host"

This repository provides a from-scratch, minimalist implementation of the Vision Transformer (ViT) in PyTorch, focusing on the core architectural pieces needed for image classification. It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. ...

Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more.

...Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures. The model generates bounding boxes and segmentation masks for each instance of an object in the image. ...

Autonomous driving is gaining increasing attention in the computer vision research community, as vision based scene understanding is key to self-driving cars. In this web page, we make image datasets public for the purpose of furthering research in scene understanding.

This project tries to include Time Adaptive Self-Organizing Map (TASOM) implementations for solving Computational Intelligence problems such as Pattern Recognition, Computer Vision, Clustering, Active Contour Modeling, and the like. The TASOM has been originally introduced for adaptive and changing environments. Several versions of TASOM networks have been introduced. Some of them are capable of changing the number of neurons based on the problems at hand.