Be the only one, not the best one

COCO-WholeBody dataset is the first large-scale benchmark for whole-body pose estimation. It is an extension of COCO 2017 dataset with the same train/val split as COCO.

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2007.11858

For each person, they annotate 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands).

Get the free ML code finder browser extension:
Chrome https://bit.ly/code_finder_chrome
Firefox https://bit.ly/code_finder_firefox.

Made With ML Topics
A collection of the best ML tutorials, toolkits and research organized by topic: https://madewithml.com/topics/
#DeepLearning #MachineLearning #Tutorials

https://www.facebook.com/groups/DeepNetGroup/permalink/1221456308247249/

Recommend a good book on statistics?

EDIT:
Ok, so to summarize for posterity:
Statistical inference by Casella
Openintro statistics
Statistics by David Freedman
All of statistics - wasserman
Applied statistics by Montgomery
And best of all :...The iron of statistic by Walid Miak

Also some courses (which I haven't checked myself):

https://www.takethiscourse.net/fundamentals-of-statistics/

https://www.edx.org/course/fundamentals-of-statistics And if you want the free MIT OCW version of this course, then search for the course 18.650 (Statistics for applications)

Thanks for the responses folks.
I think we can lock this now

• Arthur Chan

  

  You mean a "good book"? Consider to change your status text.

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Vikram Tomar

  He probably means exhaustive.

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Chandrashekhar Thejaswi

  Depends on which level, and the rigour you want..

  One good nook is

  "Statistical inference" by Casella

  

  4

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Walid Miak

  The iron of statistic 

  

  

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

  답글 7개

•  

  Imran Abdul Ghani

  Reading a book seems to be difficult... better to go for an online course.

  

  

  3

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

  답글 1개

•  

  Deep Maity

  Youtube

  

  1

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주
  •  · 

    수정됨

   

•  

  Aly Mostafa

  Openintro statistics

  

  1

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Randy Novak

  What’s my chance for finding the right book? 

  

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Ho Leung Ng

  For beginners, I like the text by David Freedman.

  

  2

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주
  •  · 

    수정됨

  답글 1개

•  

  Burhan Ahmad

  All of statistics - wasserman

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Adrian Markelov

  All of statistics by Wasserman is the ultimate book but as other have said follow a class while using it

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Sanket Mishra

  Applied statistics by Montgomery, however I preferred freedman and walpole....

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Karl Wiklund

  Depends on what you want to focus on:

  https://www.amazon.de/Probability-Random.../dp/0130200719...

   

  

  AMAZON.DE

  Probability and Random Processes With Applications to Signal Processing

  Probability and Random Processes With Applications to Signal Processing

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Harsha Nm

  Any Good online course on probability n statistics??

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

   

•  

  Ifiok EffiongBenzene Asukwo

  I think any Schuam's series is ok

   

  • 좋아요

     

  •  · 

    답글 달기

  •  · 

    공유하기

  •  · 18주

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
Kostrikov et al.: https://arxiv.org/abs/2004.13649
Code: https://github.com/denisyarats/drq
Website: https://sites.google.com/view/data-regularized-q
#DeepLearning #MachineLearning #ReinforcementLearning

COCO-WholeBody dataset is the first large-scale benchmark for whole-body pose estimation. It is an extension of COCO 2017 dataset with the same train/val split as COCO.

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2007.11858

For each person, they annotate 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands).

Get the free ML code finder browser extension:
Chrome https://bit.ly/code_finder_chrome
Firefox https://bit.ly/code_finder_firefox.

Latest from Stanford and Adobe Researchers: Inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input.
For project and code/expert/API requests: https://www.catalyzex.com/paper/arxiv:2007.11678.
This process produces motions that are significantly more realistic than those from purely kinematic methods, substantially improving quantitative measures of both kinematic and dynamic plausibility.

Python 오픈소스(Open Source) 분석 방법

https://zzsza.github.io/development/2020/07/19/opensource-analysis/?fbclid=IwAR352orsNjkEERq0g9X3ri1ogh2rfUCam8EdhLpWFOs_6l4b4nTibMtFxew

For the benefit of new folks in the field, here's a list of some well known and prestigious free online courses/books on Data Science and machine learning (for certificate you might have to pay though in some of these). Do add if you know any good free courses/resources in comments:

1) Machine Learning (Stanford University, Andrew Ng Course, Coursera)

This is a very respected course and widely accepted in machine learning industry.

https://www.coursera.org/learn/machine-learning

Do the assignments of this course in python as the course is in Octave language which isn't used that much in industry. Assignment solutions in Python.

https://github.com/dibgerge/ml-coursera-python-assignments

https://github.com/jdwittenauer/ipython-notebooks

2) Python Data Science Handbook (Jake VanderPlas)

Famous Python Data Science handbook topic-wise:

https://jakevdp.github.io/PythonDataScienceHandbook/

3) Fast.ai ML course (Jeremy Howard)

http://course18.fast.ai/ml

This course is designed by Kaggle 2 time competition winner Jeremy Howard and he is also chief scientist at Kaggle.

4) ML Course by IBM (edX)

https://www.edx.org/course/machine-learning-with-python-a-practical-introduct

5) Machine Learning Crash Course by Google

https://developers.google.com/machine-learning/crash-course/ml-intro

6) The Analytics Edge - Massachusetts Institute of Technology (MIT)

https://courses.edx.org/courses/course-v1:MITx+15.071x+1T2020/course/

7) Deep Learning (Andrew Ng, Coursera)

https://www.coursera.org/specializations/deep-learning

Latest from Max Planck researchers: State of the art in Shape and Pose Disentanglement for 3D Meshes!

For project and code/expert/API requests: https://www.catalyzex.com/paper/arxiv:2007.11341

The experiments on datasets of 3D humans, faces, hands and animals demonstrate the generality of our approach.

From #ECCV2020: Reconstruct a morphable shape, texture, and viewpoint from an image collection without 3D ground truth *and* 2D keypoints, allowing us to explore new categories like shoes!

For project and code/expert/API requests: https://www.catalyzex.com/paper/arxiv:2007.10982

Facebook released a Machine Learning algorithm (Multilevel Pixel Aligned Implicit Function For High Resolution 3D Human Digitization.) that can reconstruct or generate 3D pose by just looking at a single image. So, is this a replacement of Character Designers. What do you think guys?

Paper: https://arxiv.org/pdf/2004.00452.pdf
Code: https://github.com/facebookresearch/pifuhd
Colab: https://colab.research.google.com/drive/11z58bl3meSzo6kFqkahMa35G5jmh2Wgt

Credit: PIFuHD or Multilevel Pixel Aligned Implicit Function For Heigher Resolution 3D Human Digitization.

#MachineLearning #machinelearningalgorithms #facebookpost #AI #artificialintelligence #facebookページ

Deep Learning and Computer Vision Course List.

Computer Vision 3: Detection, Segmentation and Tracking - Technical University Munich
https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe

Stanford CS221: Artificial Intelligence: Principles and Techniques
https://www.youtube.com/playlist?list=PLuv1FSpHurUeIGea3o8H9oEsIFgw_mizO

TUM Lectures | Advanced Deep Learning
https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz

Reinforcement Learning by Deep Mind (Google) 2020
https://www.youtube.com/playlist?list=PLuv1FSpHurUe_hYTJz-cFH1zo_i6jL5JF

Statistical Machine Learning 2020: Lectures by Ulrike von Luxburg
https://www.youtube.com/playlist?list=PLuv1FSpHurUcSNZaGAkVsbwKRYKPd0sLF

Latest from Microsoft researchers: High-quality video inpainting!

For project and code/expert/API requests: https://www.catalyzex.com/paper/arxiv:2007.10247

They propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, they simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss

안녕하세요.
제가 자연어처리 입문하면서 도움되었던 자료들 공유해보려고 합니다!
딥러닝을 이용한 자연어 처리 입문 [https://wikidocs.net/book/2155]
BERT 톺아보기
[http://docs.likejazz.com/bert/#fn:fn-2]
Dissecting BERT Part 1: The Encoder
[https://medium.com/dissecting-bert/dissecting-bert-part-1-d3c3d495cdb3]
LaRva 데뷰 2019 - 엄~청 큰 언어 모델 공장 가동기!
[https://deview.kr/2019/schedule/291]
ChrisMcCormickAI 유튜브 채널
[https://www.youtube.com/watch?v=FKlPCK1uFrc&list=PLam9sigHPGwOBuH4_4fr-XvDbe5uneaf6]
[https://www.youtube.com/watch?v=l8ZYCvgGu0o]
RoBERTa 논문
[https://arxiv.org/abs/1907.11692]
ALBERT 논문 / 논문 리뷰
[https://arxiv.org/abs/1909.11942]
[https://y-rok.github.io/nlp/2019/10/23/albert.html]
Huggingface/transformers 라이브러리[https://github.com/huggingface/transformers]
[https://huggingface.co/transformers/quicktour.html]
[https://huggingface.co/models]
한국어 Tokenizer 비교
[https://blog.pingpong.us/dialog-bert-tokenizer/]
TorchText 전처리 라이브러리 사용법
[https://wikidocs.net/65348]
GPT-2 설명 / BERT랑 비교
[http://jalammar.github.io/illustrated-gpt2/]
앞으로도 꾸준히 올려보겠습니다!

Latest from Baidu researchers: Automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud!

For project and code/expert/API requests: https://www.catalyzex.com/paper/arxiv:2007.08854

In order to fill a target inpainting area in a frame, it is straightforward to transform pixels from other frames into the current one with correct occlusion.

#BERT #KcBERT

안녕하세요!

한국어 댓글 데이터셋으로 BERT Pretrain을 처음부터 진행해 만든 KcBERT를 공개합니다 :)

공개된 한국어 BERT는 대부분 한국어 위키, 뉴스 기사, 책 등 잘 정제된 데이터를 기반으로 학습한 모델입니다. 한편, 실제로 NSMC와 같은 댓글형 데이터셋은 정제되지 않았고 구어체 특징에 신조어가 많으며, 오탈자 등 공식적인 글쓰기에서 나타나지 않는 표현들이 빈번하게 등장합니다.

KcBERT는 위와 같은 특성의 데이터셋에 적용하기 위해, 네이버 뉴스에서 댓글과 대댓글을 수집해, 토크나이저와 BERT모델을 처음부터 학습한 Pretrained BERT 모델입니다.

KcBERT는 Huggingface의 Transformers 라이브러리를 통해 간편히 불러와 사용할 수 있습니다. (별도의 파일 다운로드가 필요하지 않습니다!)

좀더 자세한 내용은 아래 Github Repo를 참고해주세요! :D

[https://github.com/Beomi/KcBERT](https://github.com/Beomi/KcBERT)

EDA를 쉽게 해볼 수 있는 좋은 파이썬 라이브러리가 있습니다.

sweetviz
pandas-profiling

html 보고서도 만들어주고 그동안 고생하면서 한 것 생각하면 참 쉽고 좋아졌습니다.

모두 그 느낌을 받아보시길...

Agile Human Behavior Imitation by humanoid models!

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2006.07364

This approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3.6M) and generates diverse long-term motions.

Better than Faceapp: State of the art in Facial Attribute Editing!

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2007.05892

Existing approaches suffer from a serious compromise between correct attribute generation and preservation of the other information such as identity and background, because they edit the attributes in the imprecise area. To resolve this dilemma, we propose a progressive attention GAN (PA-GAN) for facial attribute editing.

Training and inference jupyter notebook on an ongoing kaggle competition "Global wheat detection"

Training code - https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo (Example 18)

Kaggle inference kernel - https://www.kaggle.com/abhishek4273/starter-code-using-monk-object-detection-library

The task is challenging because of
📌 overlap of dense wheat plants
📌 wind blurring the photographs
📌 wheat heads getting morphed in yellow and green backgrounds

The competition will be active for next 20-25 days.

Happy Coding!!

[Github/Repo] Pytorch Metric Learning

딥러닝 모델을 훈련 시킨다는 것은 '어떤 Loss를 어떻게 줄일것이냐' 입니다. 물론 Loss를 줄인다고 그 모델이 좋은 모델일거란 보장은 없지만요.

그리고 이 loss를 유사도, 즉 원본과의 차이를 측정하여 모델을 훈련시키는 방법이 metric learning입니다. 원본과 얼마나 유사한지 distance(loss)를 측정하는 방식은 다양합니다.

그런 다양한 방법을 Pytorch에서 사용할 수 있게 만들어둔 레포가 있어 공유합니다. triplet loss를 제외하고는 다들 저에게 생소하네요. 여기 있는 내용을 공부하며 metric에 대해 좀 더 고민해봐야겠습니다.

https://github.com/KevinMusgrave/pytorch-metric-learning

State of the art in single image super resolution!

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2007.04344

Excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a Super Resolution model, which limits their applications to Super Resolution with resource constrained devices in real world. To resolve these problems, researchers propose a lightweight enhanced Super Resolution convolutional neural network

Latest from Purdue and Chicago researchers: Low-Power Object Counting!
For project and code/API/dataset requests: https://www.catalyzex.com/paper/arxiv:2007.01369
By using a few small DNNs to process each image, this method reduces the memory requirement, inference time, energy consumption, and number of operations with negligible accuracy loss when compared with the existing object counters.

https://www.facebook.com/groups/KerasKorea/permalink/3716139575068647/

오늘은 논문대신 프로젝트를 하나 짧게 소개드립니다.

회사에서 OCR엔진을 만들기에는 너무 인력이 많이 필요하고 클라우드 OCR를 쓰기엔 제약이 있거나 공짜로 쓰고 싶을때 도움이 될만한 OCR 오픈소스입니다. (https://github.com/JaidedAI/EasyOCR)

아쉽게도 TF는 아니고 Pytorch로 되어있지만 딥러닝은 아예 몰라도되고 Python만 학생수준으로 쓸 수 있으면 전혀 상관없습니다.

이제는 많은 모델들이 나와서 상대적으로 인기가 식었지만 예전에 OCR 오픈소스로 항상 거론되던 구글이 후원한 Tesseract보다 더 높은 정확도를 보이고 다양한 언어를 제공합니다. 물론 Tesseract도 최근에는 LSTM을 도입하는 등 다양한 시도를 하고 있습니다.

EasyOCR은 네이버의 CRAFT를 Detection 모델로 사용하고 있고 CRNN을 기반으로 Recognition을 합니다.(Restnet-LSTM-CTC) 그리고 오타보정을 위한 greedy, beamsearch, wordbeamsearch도 옵션으로 제공합니다.

또한, 특이하게도 한국어, 중국어, 태국어 등을 지원하고 프리트레이닝 모델도 같이 제공합니다. 사용법도 매우 간단해서 모델개념을 알 필요 없이 자동으로 다운받아서 수행합니다. 랭귀지 모델도 업데이트를 예고하고 있고 지속적으로 업그레이드 예정이라 OCR을 심플하게 비즈니스에 적용하기에 좋은(특히 무료고 기반지식이 전무해도 되니) 오픈소스로 보입니다.

Latest from Adobe and UC Berkeley researchers: State of the art in deep image manipulation.
For project and code/API/dataset requests: https://www.catalyzex.com/paper/arxiv:2007.00653
The key idea is to encode an image into two independent components and enforce that any swapped combination maps to a realistic image.

Deep single image manipulation using conditional adversarial generators!
For project and code/API/dataset requests: https://www.catalyzex.com/paper/arxiv:2007.01289
Their network learns to map between a primitive representation of the image (e.g. edges) to the image itself. At manipulation time, their generator allows for making general image changes by modifying the primitive input representation and mapping it through the network.

Amazon에서 MXNet 기반으로 작성한 유명한 텍스트북 Dive Into Deep Learning 에 PyTorch 버전의 코드들이 수록된 것이 얼마 지나지 않아서, 이번에는 TensorFlow 버전의 코드들이 빠르게 추가되고 있습니다.

오늘부로 7장까지의 내용에 대하여 TensorFlow 코드가 수록되었다는 소식입니다. 따라서, MXNet 을 제외하고, PyTorch 기반으로 작성된 부분까지를 TensorFlow로 동일하게 커버하게 된 것이군요.

7장까지의 내용이 아주 Advanced 된 것은 아니지만, 기본을 다지는데는 좋은 내용으로 구성된 것으로 보여집니다. 대충 보자면 아래와 같은 챕터로 구성되어 있군요
Introduction
Preliminaries
Linear Neural Network
Multilayer Perceptrons
Deep Learning Computation
Convolutional Neural Networks
Modern Convolutional Neural Networks

8장부터 후반부의 내용은 RNN을 포함하여, Attention, BERT 등을 포함한 NLP 모델에 대한 내용과 최적화 알고리즘, GAN 등 다른분야 및 좀 더 심화된 내용이 포함됩니다.

이 정도 속도로 추가된다고 볼 때, 연말 내로는 MXNet / PyTorch / TensorFlow 메인스트림 프레임워크를 모두 커버하는 최초의 책이 탄생하지 않을까 기대해 봅니다.

책 사이트: http://d2l.ai/index.html


좋은 정보 감사합니다! 이 책 번역본도 워낙 유명해서 아실텐데요!
https://ko.d2l.ai/

PyTorch3D 입문 강좌(영어)

PyTorch3D를 간략하게 소개하는 영상입니다.

PyTorch3D는 올해 2월에 3차원 대상을 다루기 위한 PyTorch의 공식 라이브러리로 발표되었지요.

PyTorch3D 공식홈 https://pytorch3d.org/

https://www.youtube.com/watch?v=Pph1r-x9nyY

https://www.facebook.com/groups/computervisionandimageprocessing/permalink/3083736145029353/

Using [#ComputerVision](https://twitter.com/hashtag/ComputerVision?src=hashtag_click) to control a teddy [#robot](https://twitter.com/hashtag/robot?src=hashtag_click) avatar

[#AI](https://twitter.com/hashtag/AI?src=hashtag_click) [#Robotics](https://twitter.com/hashtag/Robotics?src=hashtag_click) [#ML](https://twitter.com/hashtag/ML?src=hashtag_click) [#MachineLearning](https://twitter.com/hashtag/MachineLearning?src=hashtag_click)

Deep Learning with Keras Series By Ali Masri
1. Deep Learning with Keras Tutorial https://www.marktechpost.com/2019/06/11/deep-learning-with-keras-tutorial-part-1/
2. Data Pre-processing for Deep Learning models https://www.marktechpost.com/2019/06/14/data-pre-processing-for-deep-learning-models-deep-learning-with-keras-part-2/
3. Regression with Keras https://www.marktechpost.com/2019/06/17/regression-with-keras-deep-learning-with-keras-part-3/
4. Classification https://www.marktechpost.com/2019/06/24/deep-learning-with-keras-part-4-classification/
5. Convolutional Neural Networks https://www.marktechpost.com/2019/07/04/deep-learning-with-keras-part-5-convolutional-neural-networks/
6. Textual Data Preprocessing https://www.marktechpost.com/2019/09/13/deep-learning-with-keras-part-6-textual-data-preprocessing/
7. Recurrent Neural Networks https://www.marktechpost.com/2019/10/01/deep-learning-with-keras-part-7-recurrent-neural-networks/