Machine Learning University: Accelerated Computer Vision Class

Overview

logo

Machine Learning University: Accelerated Computer Vision Class

This repository contains slides, notebooks, and datasets for the Machine Learning University (MLU) Computer Vision class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with Computer Vision, learn about widely used Machine Learning techniques, and apply them to real-world problems.

YouTube

Watch all Computer Vision class video recordings in this YouTube playlist from our YouTube channel.

Playlist

Course Overview

There are three lectures and one final project for this class.

Lecture 1 Lecture 2 Lecture 3
Intro to ML Image Datasets Advanced CNNs: VGGNet and ResNet
Intro to Computer Vision Training Neural Networks Object Detection
Neural Networks Modern CNNs: LeNet and AlexNet Semantic Segmentation
Convolutional Neural Networks Model fine-tuning

Final Project: Practice working with a "real-world" computer vision dataset for the final project. Final project dataset is in the data/final_project_dataset folder. For more details on the final project, check out this notebook.

Contribute

If you would like to contribute to the project, see CONTRIBUTING for more information.

License

The license for this repository depends on the section. Data set for the course is being provided to you by permission of Amazon and is subject to the terms of the Amazon License and Access. You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this course. The lecture slides are released under the CC-BY-SA-4.0 License. The code examples are released under the MIT-0 License. See each section's LICENSE file for details.

Owner
AWS Samples
AWS Samples
A Lightweight Hyperparameter Optimization Tool πŸš€

Lightweight Hyperparameter Optimization πŸš€ The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machin

136 Jan 08, 2023
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
Custom implementation of Corrleation Module

Pytorch Correlation module this is a custom C++/Cuda implementation of Correlation module, used e.g. in FlowNetC This tutorial was used as a basis for

ClΓ©ment Pinard 361 Dec 12, 2022
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Fast Symbolic Regression Symbolic Regression is a non-linear, non-parametric Machine Learning method capable of modeling complex data sets. fastsr aim

VAMSHI CHOWDARY 3 Jun 22, 2022
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

BEGAN in Tensorflow Tensorflow implementation of BEGAN: Boundary Equilibrium Generative Adversarial Networks. Requirements Python 2.7 or 3.x Pillow tq

Taehoon Kim 922 Dec 21, 2022
A flexible and extensible framework for gait recognition.

A flexible and extensible framework for gait recognition. You can focus on designing your own models and comparing with state-of-the-arts easily with the help of OpenGait.

Shiqi Yu 335 Dec 22, 2022
Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

Lucas 103 Dec 14, 2022
πŸ€ Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

πŸ€ Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

xmu-xiaoma66 7.7k Jan 05, 2023
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

Open-L2O This repository establishes the first comprehensive benchmark efforts of existing learning to optimize (L2O) approaches on a number of proble

VITA 161 Jan 02, 2023
PyTorch experiments with the Zalando fashion-mnist dataset

zalando-pytorch PyTorch experiments with the Zalando fashion-mnist dataset Project Organization β”œβ”€β”€ LICENSE β”œβ”€β”€ Makefile - Makefile with co

Federico Baldassarre 31 Sep 25, 2021
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend

Holy Wu 44 Dec 27, 2022
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

πŸ“– Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) πŸ”₯ If DaGAN is helpful in your photos/projects, please hel

Fa-Ting Hong 503 Jan 04, 2023
Hi Guys, here I am providing examples, which will help you in Lerarning Python

LearningPython Hi guys, here I am trying to include as many practice examples of Python Language, as i Myself learn, and hope these will help you in t

4 Feb 03, 2022
Algo-burn - Script to configure an Algorand address as a "burn" address for one or more ASA tokens

Algorand Burn Address This is a simple script to illustrate how a "burn address"

GSD 5 May 10, 2022
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Yu Meng 60 Dec 30, 2022
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

Aitor Ruano 87 Dec 16, 2022
Conjugated Discrete Distributions for Distributional Reinforcement Learning (C2D)

Conjugated Discrete Distributions for Distributional Reinforcement Learning (C2D) Code & Data Appendix for Conjugated Discrete Distributions for Distr

1 Jan 11, 2022
A python script to lookup Passport Index Dataset

visa-cli A python script to lookup Passport Index Dataset Installation pip install visa-cli Usage usage: visa-cli [-h] [-d DESTINATION_COUNTRY] [-f]

rand-net 16 Oct 18, 2022
This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression Introduction In this paper, we are interested in the bottom-up paradigm of estima

HRNet 367 Dec 27, 2022