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2017-learning-to-run

The Winning Solution for the Learning To Run Challenge 2017

activeragdollstyletransfer

Research into locomotion style transfer with Active Ragdolls (using MarathonEnvs +ml_agents)

ai-interview-cards

最完整的AI算法面试题目仓库，1000道，25个类目

algorithm-pattern-python

Python version of algorithm-pattern

cg

computer graphics homework

clip-italian

CLIP (Contrastive Language–Image Pre-training) for Italian

clip-multilingual

Multilingual CLIP - Semantic Image Search in 100 languages

course

deepmvs

DeepMVS: Learning Multi-View Stereopsis

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

dr

file

github-slideshow

A robot powered training repository :robot:

kelip

Official PyTorch implementation of "Large-scale Bilingual Language-Image Contrastive Learning" (ICLRW 2022)

models

Models built with TensorFlow

multilingual-clip

OpenAI CLIP text encoders for multiple languages!

nlp_crawler1

Crawled professors' info and developed a parser based on Stanford open source tools

noisynet-a3c

Noisy Networks for Exploration

noisynet-dqn

Tensorflow Implementation for "Noisy network for exploration"

osim-rl

Reinforcement learning environments with musculoskeletal models

planets

planets in milky way

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

run-skeleton-run

Reason8.ai PyTorch solution for NIPS RL 2017 challenge

superglue-pytorch

[SuperGlue: Learning Feature Matching with Graph Neural Networks] This repo includes PyTorch code for training the SuperGlue matching network on top of SIFT keypoints and descriptors.

supergluepretrainednetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

tensorpack

Neural Network Toolbox on TensorFlow

ucla-cs-32

These are my solutions for the four projects and five homeworks from UCLA CS 32 Spring 2015 with Prof Smallberg. These are my own solutions and are therefore not perfect. The source code for the various projects should only be used as a vague guideline to help you if you are stuck. Do not copy directly from these files as they will result in your own penalisation!