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Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo…
Machine Learning and Computer Vision Engineer - Technical Interview Questions
(GUI-多平台支持) B站 哔哩哔哩 视频下载器。支持稍后再看、收藏夹、UP主视频批量下载|Bilibili Video Downloader 😳
Neural Pre Processing is an end-to-end weakly supervised learning approach for converting raw head MRI images to intensity-normalized, skull-stripped brain in a standard coordinate space
A collection of full time roles in SWE, Quant, and PM for new grads.
Collection of Summer 2026 tech internships!
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
An open source implementation of CLIP.
A collection of resources on applications of multi-modal learning in medical imaging.
A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Jupyter notebooks and other materials developed for the Columbia course APMA 4300
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
Official implementation of the paper "You Only Need Adversarial Supervision for Semantic Image Synthesis" (ICLR 2021)
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised lear…
The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)