Course content and resources for the AIAIART course.

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Deep Learningaiaiart
Overview

AIAIART course

This repo will house the notebooks used for the AIAIART course. Part 1 (first four lessons) ran via Discord in September/October 2021. Part 2 started Saturday April 9th at 4pm UTC. Join the Discord linked below if you'd like to join the chat and get reminders for the livestream! Streams run on https://www.twitch.tv/johnowhitaker and more concise videos tend to go up on YouTube the following day.

The videos for past lessons: https://www.youtube.com/playlist?list=PL23FjyM69j910zCdDFVWcjSIKHbSB7NE8

Notebooks:

Feel free to reach out to me @johnowhitaker on Twitter with feedback, questions or requests. And come join our discord to share your work and chat with others doing similar projects: https://discord.gg/vSjhr8xb4g. The Discord is the best way to hear about upcoming lessons and livestreams.

Owner
Jonathan Whitaker
Data Scientist and independent researcher based in Zimbabwe.
Jonathan Whitaker
Code for the paper "Reinforced Active Learning for Image Segmentation"

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Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

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Towards Interpretable Deep Metric Learning with Structural Matching

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A curated list of resources for Image and Video Deblurring

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NeurIPS 2021 Datasets and Benchmarks Track

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potpourri3d - An invigorating blend of 3D geometry tools in Python.

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Python code for loading the Aschaffenburg Pose Dataset.

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The repo for reproducing Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study

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Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

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R-Drop: Regularized Dropout for Neural Networks

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Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

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Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

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A curated list of awesome projects and resources related fastai

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Malware Analysis Neural Network project.

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Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

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