Machine Learning University: Accelerated Computer Vision Class

Related tags

Deep Learning python machine-learning computer-vision deep-learning mxnet gluon gluoncv
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.

Comments
  • Requirements file and re-run of the notebooks

    Requirements file and re-run of the notebooks

    Added requirements file, tested the notebooks and updated the Autogluon notebook to the latest API.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 3
  • Broken URL

    Broken URL

    from main readme: 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.

    https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv/blob/master/notebooks/MLA-CV-Lecture1-Final-Project.ipynb

    opened by robmarkcole 2
  • Added Open Studio Lab button at README.md

    Added Open Studio Lab button at README.md

    Issue #, if available:

    Description of changes:

    • Added Open Studio Lab button at README.md

    I made sure all buttons works.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by wildgeece96 1
  • Corrected nb names and add environment file

    Corrected nb names and add environment file

    Notebook names are changed according to the recordings and a new environment file is added.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 1
  • alkasr3

    alkasr3

    Please don't الكاسر فيصل من كبارر المرمجن والمطورين في التطبيقات ودروس الهكر وكيفيه استخدام الخصوصيه ودعم الشركات واهو الكاسر فيس بوك قناتي اليوتيوب تطبيقاتي المشرك بها #### __####_#####““####

    opened by alkasr999 1
  • Create a Website for this Repo

    Create a Website for this Repo

    Hi there.

    Is there a website for this repo?

    Because if you don't have, well, this repo can simply be turned into a website right away. Others will discover this project in that website.

    Steps:

    1. Go to Settings and look for GitHub Pages, scroll down. That's almost at the bottom.

    2. You will see there: Branch:none, so you should change that to master because you have a README.md file in the master repo. This will be your page. Click Save first.

    3. Then click Choose a theme, you select a predefined theme of your site.

    4. Visit your site now! The URL will be https://aws-samples.github.io/aws-machine-learning-university-accelerated-cv.

    If you were amazed by that, simply read the documentation about GitHub Pages.

    opened by jdevstatic 1
  • Corrected notebook link in readme

    Corrected notebook link in readme

    Final project notebook link on readme was broken. It is corrected.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 0
  • Pytorch cv

    Pytorch cv

    Merge Pytorch changes.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 0
  • [PyTorch]: Add Lec 1 Neural Nets

    [PyTorch]: Add Lec 1 Neural Nets

    Description of changes: Add pytorch port lecture 1 neural nets.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by AnirudhDagar 0
  • Updating mxnet-cu101mkl import

    Updating mxnet-cu101mkl import

    MXNet library import correction. Changed from mxnet-cu101mkl==1.6.0 to mxnet-cu101mkl==1.6.0.post0.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 0
  • Both GPU and CPU enable

    Both GPU and CPU enable

    Major modification:

    • initialize NN with defined context: net.initialize(init=init.Xavier(), ctx=context)
    • move validation dataset to defined context:
      "X_val = nd.array(X_val).as_in_context(context)\n", "y_val = nd.array(y_val).as_in_context(context)"
    opened by goldmermaid 0
  • Bump setuptools from 58.4.0 to 65.5.1

    Bump setuptools from 58.4.0 to 65.5.1

    Bumps setuptools from 58.4.0 to 65.5.1.

    Release notes

    Sourced from setuptools's releases.

    v65.5.1

    No release notes provided.

    v65.5.0

    No release notes provided.

    v65.4.1

    No release notes provided.

    v65.4.0

    No release notes provided.

    v65.3.0

    No release notes provided.

    v65.2.0

    No release notes provided.

    v65.1.1

    No release notes provided.

    v65.1.0

    No release notes provided.

    v65.0.2

    No release notes provided.

    v65.0.1

    No release notes provided.

    v65.0.0

    No release notes provided.

    v64.0.3

    No release notes provided.

    v64.0.2

    No release notes provided.

    v64.0.1

    No release notes provided.

    v64.0.0

    No release notes provided.

    v63.4.3

    No release notes provided.

    v63.4.2

    No release notes provided.

    ... (truncated)

    Changelog

    Sourced from setuptools's changelog.

    v65.5.1

    Misc ^^^^

    • #3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok
    • #3659: Fixed REDoS vector in package_index.

    v65.5.0

    Changes ^^^^^^^

    • #3624: Fixed editable install for multi-module/no-package src-layout projects.
    • #3626: Minor refactorings to support distutils using stdlib logging module.

    Documentation changes ^^^^^^^^^^^^^^^^^^^^^

    • #3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

    Misc ^^^^

    • #3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).
    • #3576: Updated version of validate_pyproject.

    v65.4.1

    Misc ^^^^

    • #3613: Fixed encoding errors in expand.StaticModule when system default encoding doesn't match expectations for source files.
    • #3617: Merge with pypa/distutils@6852b20 including fix for pypa/distutils#181.

    v65.4.0

    Changes ^^^^^^^

    v65.3.0

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Bump wheel from 0.37.0 to 0.38.1

    Bump wheel from 0.37.0 to 0.38.1

    Bumps wheel from 0.37.0 to 0.38.1.

    Changelog

    Sourced from wheel's changelog.

    Release Notes

    UNRELEASED

    • Updated vendored packaging to 22.0

    0.38.4 (2022-11-09)

    • Fixed PKG-INFO conversion in bdist_wheel mangling UTF-8 header values in METADATA (PR by Anderson Bravalheri)

    0.38.3 (2022-11-08)

    • Fixed install failure when used with --no-binary, reported on Ubuntu 20.04, by removing setup_requires from setup.cfg

    0.38.2 (2022-11-05)

    • Fixed regression introduced in v0.38.1 which broke parsing of wheel file names with multiple platform tags

    0.38.1 (2022-11-04)

    • Removed install dependency on setuptools
    • The future-proof fix in 0.36.0 for converting PyPy's SOABI into a abi tag was faulty. Fixed so that future changes in the SOABI will not change the tag.

    0.38.0 (2022-10-21)

    • Dropped support for Python < 3.7
    • Updated vendored packaging to 21.3
    • Replaced all uses of distutils with setuptools
    • The handling of license_files (including glob patterns and default values) is now delegated to setuptools>=57.0.0 (#466). The package dependencies were updated to reflect this change.
    • Fixed potential DoS attack via the WHEEL_INFO_RE regular expression
    • Fixed ValueError: ZIP does not support timestamps before 1980 when using SOURCE_DATE_EPOCH=0 or when on-disk timestamps are earlier than 1980-01-01. Such timestamps are now changed to the minimum value before packaging.

    0.37.1 (2021-12-22)

    • Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)
    • Fixed parsing of file names containing commas in RECORD (PR by Hood Chatham)

    0.37.0 (2021-08-09)

    • Added official Python 3.10 support
    • Updated vendored packaging library to v20.9

    ... (truncated)

    Commits
    • 6f1608d Created a new release
    • cf8f5ef Moved news item from PR #484 to its proper place
    • 9ec2016 Removed install dependency on setuptools (#483)
    • 747e1f6 Fixed PyPy SOABI parsing (#484)
    • 7627548 [pre-commit.ci] pre-commit autoupdate (#480)
    • 7b9e8e1 Test on Python 3.11 final
    • a04dfef Updated the pypi-publish action
    • 94bb62c Fixed docs not building due to code style changes
    • d635664 Updated the codecov action to the latest version
    • fcb94cd Updated version to match the release
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Owner
AWS Samples
AWS Samples
GitHub
Aalto-cs-msc-theses - Listing of M.Sc. Theses of the Department of Computer Science at Aalto University

Aalto-CS-MSc-Theses Listing of M.Sc. Theses of the Department of Computer Scienc

Jorma Laaksonen 3 Jan 27, 2022
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

H2O.ai 6.1k Jan 5, 2023
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algorithms that do the job in the least jargon possible and examples to guide you through every step of the way.

Anish 324 Dec 27, 2022
GPU-Accelerated Deep Learning Library in Python

Hebel GPU-Accelerated Deep Learning Library in Python Hebel is a library for deep learning with neural networks in Python using GPU acceleration with

Hannes Bretschneider 1.2k Dec 21, 2022
Accelerated deep learning R&D

Accelerated deep learning R&D PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and

Catalyst-Team 3.1k Jan 6, 2023
meProp: Sparsified Back Propagation for Accelerated Deep Learning

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

LancoPKU 107 Nov 18, 2022
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine

null 419 Jan 3, 2023
meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

LancoPKU 107 Nov 18, 2022
PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{CV2018, author = {Donny You ([email protected])}, howpubl

Donny You 40 Sep 14, 2022
A PyTorch-Based Framework for Deep Learning in Computer Vision

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

Donny You 2.2k Jan 9, 2023
[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

Ayan Kumar Bhunia 44 Dec 12, 2022
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

null 52 Dec 23, 2022
Deep Learning for Computer Vision final project

Deep Learning for Computer Vision final project

grassking100 1 Nov 30, 2021
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Monk - A computer vision toolkit for everyone Why use Monk Issue: Want to begin learning computer vision Solution: Start with Monk's hands-on study ro

Tessellate Imaging 507 Dec 4, 2022
Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo

Jacob Gildenblat 6.6k Jan 6, 2023
3D ResNet Video Classification accelerated by TensorRT

Activity Recognition TensorRT Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT P.S Click on

Akash James 39 Nov 21, 2022
Hardware accelerated, batchable and differentiable optimizers in JAX.

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Google 621 Jan 8, 2023
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Alexander Amini 75 Dec 15, 2022
Implements pytorch code for the Accelerated SGD algorithm.

AccSGD This is the code associated with Accelerated SGD algorithm used in the paper On the insufficiency of existing momentum schemes for Stochastic O

null 205 Jan 2, 2023