에서 'Rename file cusolver64_11.dll To cusolver64_10.dll '
2021-03-16 19:26:14.435563: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 2021-03-16 19:26:14.498628: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-03-16 19:26:14.499003: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll 2021-03-16 19:26:14.527712: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll 2021-03-16 19:26:14.532245: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll 2021-03-16 19:26:14.535978: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found 2021-03-16 19:26:14.585485: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
ERROR: Could not install packages due to an EnvironmentError: [WinError 5] 액세스가 거부되었습니다: 'C:\ProgramData\Anaconda3\envs\venv20\Lib\site-packages\tensorflow\lite\experimental\microfrontend\python\ops\_audio_microfrontend_op.so'
Consider using the `--user` option or check the permissions.
https://www.myheritage.com/deep-nostalgia MyHeritage 라는 독일 회사에서 개발한 Deep Nostalgia 라는 엔진이라고 합니다 오래된 흑백 사진을 애니매이션화 해준다고 해서 예전에 뽑아본 blind face restoration 결과를 입력으로 넣어봤는데요. 완벽해 보이지는 않지만 흥미로운 결과들이 나오네요 :)
안녕하세요 캐글코리아!! 올해 1월부터 매달 열리는 playground 대회인 Tabular Playground Series - 2월 대회가 끝이 났습니다. 1433팀 중 6위를 해서 기쁜 마음에 공유해봅니다! (나름 한국 1등이네요 ㅎㅎ) 이 대회는 간단한 tabular data를 이용해서 예측하는 regression 문제입니다. 주로 LGBM같은 GBDT 모델들을 사용합니다. 저도 LGBM 모델을 사용했습니다. 높은 점수의 핵심은 semi-supervised learning의 일종인 pseudo labelling을 사용한 것이었습니다. test data를 최대한 잘 학습시킨 이후에, 그 학습시킨 데이터까지 포함하여 다시 train을 시키는 방법입니다. 보통 train data가 부족할 때 사용하지만 이 대회에서는 성능 향상에 매우 적합했습니다. 그래서 ensemble 없이 하나의 LGBM 모델만으로 높은 순위를 달성할 수 있었습니다. 제 코드입니다. https://www.kaggle.com/vkehfdl1/6th-place-solution-pseudo-labelling-lgbm 더불어서 이번 대회 1,2,3등은 DAE를 사용했습니다. DAE는 Denoising Auto Encoder로 노이즈를 포함한 feature를 반복적으로 학습시키는 것인데요. 이 auto encoder 뉴럴 넷의 hidden layer의 weight들을 feature로 사용하는 방식입니다. Tabular data에서는 보통 GBDT 모델이 성능이 잘 나오는데, 이 DAE 방식으로 활용으로 도저히 GBDT 모델로는 상상도 못하는 성능이 나오더라고요. 1위 분의 코드와 설명을 보며 저도 열심히 공부 중입니다. 한국 캐글러 분들 항상 응원합니다! 더 노력하는 캐글러가 되어야 겠습니다.
축하드려요! 참고로 1등은 DAE를 사용했는데, 2등은 보통 GBM+NN 앙상블을 사용했습니다. Bojan이 자신의 8등 솔루션을 공유하면서 1/2/3등이 DAE를 사용했을 것이다...라고 언급을 했었는데, 실제 2등인 Dave E (지난 1월 TPS 4등)이 자신은 DAE 사용할 시간이 없었다고 했죠. DAE가 두 달 연속 TPS 대회에서 1등을 한 것을 보니 다음 대회에서는 DAE 기반 솔루션이 많이 나올 것으로 보입니다. 다음 달에도 좋은 성적 거두시길 바랍니다!
#kerasexamples #모든예제 https://keras.io/examples/ 에 가보니 정말 많은 예제들이 만들어져 있네요. Knowledge Distillation 그리고 최근에 나온 VIT, Switch Transformer까지 있네요. (며칠전에 허깅페이스에서 switch transfoemer 구현해달라는 issue를 본듯한데요. 3번째 이미지). 이 예제들은 한번씩 읽어 보시기에 너무 좋을듯 합니다.
https://youtu.be/Y2K13XDqwiM 을 보니 이런 코드를 하나씩 골라서 설명을 하는데 저희 TF-KR 의 PR12 처럼 10여명 함께 팀으로 KR12 (Keras example Reading) 만들어서 예제 하나씩 설명해보고 또 이 예제를 어디 사용할수 있는지 응용한두게 찾아서 적용해보는것을 해볼까요? 요즈음 AI교육을 많이 하시던데 좋은 교제일듯 합니다.
KR12 관심있으신분들 아래 댓글로 남겨주시면 teaming 해서 PR12처럼 KR12 한번 달려보도록 하겠습니다. (12분이 Zoom으로 모여서 한주에 예제 2~3개 설명하고 토론하고 그 영상을 공개하는 모임입니다.)
The best machine learning course I have worked on till now is the Andrew Ng's machine learning course in Coursera. You will find the link to the working examples of almost all the machine learning method of his course in this article. It's a free machine learning course. #machinelearning #datascience #python
[ TF Everywhere 행사 영상 및 메이킹 영상 공유] 안녕하세요! 어제 날짜로 TF Everywhere 텐플마을에 오신것을 환영합니다 행사가 성공적으로 마무리 되었습니다. 와-아! 게더타운부터 유튜브 스트리밍까지 700🔥이 넘는 뷰를 달성하며 많은 분들께서 관심을 가져주셨는데요. 👾게더 타운에서 사과찾기, 9와 3/4 공간 찾기👾등 팝업이벤트도 너무 빠르게 성공 해주셔서 역시 개발자 컨퍼런스 답다~ 싶게 열정이 뿜뿜!! 저도 많이 배워간 행사였습니다. (본론) 유튜브 스트리밍으로 각 세션을 참관하셨지만 안타깝게 게더 타운 티켓을 겟하지 못하신 분들을 위해! 이 행사에 대해 짤막하게 메이킹 영상을 만들어보았습니다! 더불어 병렬적으로 진행된 네가지 세션에 대한 업로드 영상도 함께 공유하니 현장에서 멀티 세션을 모두 보지 못해 아쉬웠던 분들 역시 보시면 좋을 것 같네요 :)
모든 스피커분들 감사드리고, 행사에 큰 도움 주신 권순선, 김나연 님 너무 감사드립니다! 좋은 주말 되세요!
안녕하세요! 카사바 잎 질병 분류 대회(Cassava Leaf Disease Classification Competition)가 끝나고 개인적으로 코드 정리하고 있는데 같이 공유하면 좋을 것 같아 영상을 제작하고 있습니다. (Pytorch로 작성하고 있지만 도움이 되는 부분들이 있을 것 같아 염치불구하고 공유드립니다.ㅎㅎ)
영상에서는 학습(Training) 파이프라인과 추론(Inference)에 대한 내용을 주로 다룹니다. 비어있는 코드를 처음부터 하나하나 채워가는 형식으로 만들고 있어서 공부하고 싶으신 분들에게 도움이 되지 않을까 합니다! (고수 분들은 재미가 없으실 겁니다.ㅎㅎ) 파이프라인 작성이 끝나면, 최근에 핫한 Vision Transformer (ViT) 사용법과 대회에서 공유된 상위권 솔루션들도 가볍게 이야기를 해볼까 하는데, 관심 있으신 분들에게 도움이 되길 바랍니다! Intro https://www.youtube.com/watch?v=7wdqASYZBls&t=5s 개요 및 데이터 설명 https://www.youtube.com/watch?v=pWhA7V0L1SE 데이터 로드 및 기본 설정 https://www.youtube.com/watch?v=wp3cUKEM5Xk&t=1065s Cross-Validation (CV) Split https://www.youtube.com/watch?v=pWhA7V0L1SE 이후 추가 예정! p.s. 영상 제작이 처음이라 부족한 점이 많네요. orz
요즘 컴퓨터 비전계를 뜨겁게 달구고 있는 모델이 있습니다. 바로 자연어 처리에서 이제는 대세로 자리잡은 Transformer 입니다. 지금까지는 거의 모든 모델이 Convolutional Neural Network 기반의 Architecture가 주를 이뤘는데 작년부터 점점 성능 격차가 줄어들기 시작하면서 빠르게 성장하고 있어서 최근 저도 Transformer 기반 연구들을 공부하고 있는데요,
보통 낯선 분야에 대해 공부를 할때 저는 잘 정리가 된 Survey Paper를 하나 잡아서 진득하게 파는 편입니다. 운 좋게도 올해 1월에 "Transformers in Vision: A Survey"라는 제목의 Survey 논문이 공개되어서 이를 읽고 차근 차근 정리해보았습니다.
논문 링크: https://arxiv.org/abs/2101.01169 블로그 글: https://hoya012.github.io/blog/Vision-Transformer-1/
논문 자체의 분량이 많아서 한편에 정리하려다 여러 편으로 나누게 되었으며, 이번 편에서는 Transformer에 대해 간략하게 정리하고, CNN과 대비해서 어떠한 장,단점을 갖는지 살펴본 뒤, Image Classification에 Self-Attention과 Transformer가 적용된 주요 연구들을 정리했습니다. 공부하시는데 도움이 되었으면 좋겠습니다.
P. S. 이번 글을 작성하면서 공부하는데 PR-12 스터디의 발표 영상들이 큰 도움이 되었는데요, 현재 PR-12 스터디 신규 인원 모집 중이니 많은 관심 부탁드립니다!
Poor smartphone photo scans are really annoying and these researchers finally figured out how to fix the quality! (Checkout code implementation inside link) https://www.catalyzex.com/paper/arxiv:2102.06120
👇 Free extension to get code for ML papers (❤️' by Andrew Ng) Chrome: https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil Firefox: https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex
Finally a dataset for virtual hair editing and hairstyle classification! https://www.catalyzex.com/paper/arxiv:2102.06288
👇 Free extension to get code for ML papers (❤️' by Andrew Ng) Chrome: https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil Firefox: https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex
State of the art in image manipulation (stylegan) https://www.catalyzex.com/paper/arxiv:2102.02766
👇 Free extension to get code for ML papers (❤️' by Andrew Ng) Chrome: https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil Firefox: https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex
말씀하신대로 적당한 파이썬 시계열 분석 책을 저도 못 찾아서 제가 일할 때 공부했던 방법을 공유드립니다 :)
저는 학부에서 R로 시계열분석을 배웠고, 회사에서 개발할 때 저 책으로 시계열 데이터 전처리를 한 다음에 모형 적합 및 시각화는 statsmodels 패키지 및 구글링으로 처리하기는 했습니다. 어차피 모형 적합은 코드 몇 줄이면 되고 전처리가 대다수라서 저 책으로 시계열 데이터를 익숙하게 핸들링 할 수 있다면, 이론을 이미 아는 상태에서는 원하는 분석을 쉽게 하실 수 있을 거에요 :)
아니면 파이썬으로 금융데이터 다루는 책들을 찾아보셔도 되지 않을까 생각해봅니다 :) 본 적은 없지만, 금융데이터도 시계열 데이터니까요
Create a Game Character Face from a Single Portrait! https://www.catalyzex.com/paper/arxiv:2102.02371
👇 Free extension to get code for ML papers (❤️' by Andrew Ng) Chrome: https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil Firefox: https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex
안녕하세요! 질문이 있습니다. 노트북 쓰다보면 패키지를 인스톨해서 쓰는 경우가 있는데, 매번 패키지를 인스톨하는걸 피할 수 있는 방법이 있나요?
예를들어 현재 작업하고 있는 노트북에 설치된 패키지들을 저장해서 다음 새로운 노트북을 열었을때 똑같은 환경이 열리도록 만드는 방법을 알고 싶습니다!
Issac Lee 캐글 노트북에서 !wget 깃헙에있는파일주소 하시면 되는데요. https://towardsdatascience.com/4-awesome-ways-of-loading-ml-data-in-google-colab-9a5264c61966 여기서 2번 하시면 똑같이 돼요.
그러니까 깃헙에 관련 패키지 인스톨 파일을 .py형태로 잘 정리해서 올리시고, 그 url을 가져오셔서 캐글노트북에 저 블로그 2번처럼 하시면 완성!
[AP & mAP 내용 정리] 분류기의 성능 평가를 위한 지난 포스팅(정밀도(Precision)와 재현율(Recall) 내용 정리)에 이어 이번엔 객체 검출기의 성능 평가를 위한 AP와 mAP에 대한 내용을 정리해보았습니다. https://pacientes.github.io/posts/2021/02/ml_ap_map/
ML 고수분들께 질문드립니다! 딥러닝 공부를 해오면서 요즘 더욱 더 기본기의 중요성을 느끼고 있는데 기본기를 직접 구현을 통해 복습해보려고 하는데 직접 구현해볼만한 내용들이 어떤게 있을까요??? ML 기본기를 탄탄하게 하기 위해 직접 구현해보면서 복습해보려고 합니다!! 많이 추천 부탁드릴게요!!ㅎㅎ
자신의 주위에서 흥미로운 문제를 찾아보세요. 우리나라 각 기관에서 공개한 데이터도 많습니다. 이 단계, problem finding이 제일 중요합니다. 기술이나 도구는 보편화됩니다. 갈수록. 예를 들자면 hwp, word, LaTex..등 도구를 몰라서 좋은 글을 못쓰는 것이 아니거든요. 다른 사람의 (코드,데이터) 깃헙에서 내려받아 백날 돌려봐도 큰 도움이 안됩니다. 아주 작은 데이터라도, 본인이 진짜 궁금해하는 문제부터 시작해보세요. 그래야 모형평가도 가능하고 새로운 모형으로 개선할 수 있습니다. ML에서 general solution은 없거든요. 그런 "통일장 이론"이 목표이긴 하지만, 크게보면 결국은 NP 공간의 local에 빠질 수 밖에 없죠. 요약: 내가 잘 알고있는 "나와바리"에서 일단 시작하자.
#SLAM #Study #SLAMDUNK #Season2 #Complete Online SLAM Study SLAM Dunk Season 2가 총 13주의 스터디를 끝으로 마무리가 되었습니다ㅎ 양질의 발표 준비해주신 스터디 참가원 분들께 감사드리고 앞으로도 더욱 유익하고 재미있는 컨텐츠로 찾아뵙도록 하겠습니다!
* 참여자분들의 질문&답변, 디스커션이 포함된 영상을 원하시는 분들은 아래의 구글 폼을 작성해주시면 영상 링크를 전달드리도록 하겠습니다! https://docs.google.com/forms/d/e/1FAIpQLSeip8TIpjeK-q8jSH3llwVSe87RLGqfuSLd4W3diClELcs7Yg/viewform
Self-Supervised 계열의 paper를 정리하려는 목적으로 만든 repository에 꾸준히 스타가 생기고 있어서 기분이 좋아 정리를 할 겸 그림을 그려보았습니다.
혹시나, Self-Supervised 계열의 방법론에 관심이 있으셨던 분들은 참고해주시면 좋을 듯 하네요.
확실히 최근들어서는 contrastive learning 계열의 방법들이 주를 이루며 달려가고 있는 것 같습니다.
+ 최근에는 negative sample에 대한 비판적인 시각과 함께 positive sample 만을 활용하는 방법론들이 각광받고 있는 것 같습니다. 또 6개월 후의 모습은 어떠할지 벌써 기대가 되는군요.
+ Dense 라고 표기해놓은 방법들은 pixel-level 로 contrastive learning을 접근하였습니다. 최근에 흥미가 많이 가는 분야입니다. 영상의 제일 기본적인 가정인 '인접 픽셀은 유사한 성격을 지닐 것이다.' 를 잘 살리지 않았나 하는 생각이 드네요.
+ 레포는 꾸준히 업데이트를 하려고 노력하고 있습니다.
+ GPU로 몰아붙이는 학습의 새로운 매커니즘이 생길까봐 무섭지만, 늘 그렇듯 어디선가에서는 효율적인 학습의 패러다임을 제시하는 방법이 등장할 것이라 믿습니다 (SimCLR --> MoCO, ViT-->DeiT). 언뜻보니 구글과 페이스북의 구도인가요..? ㅎㅎ
안녕하세요! NLP를 공부하시는 분들, 혹은 처음 접하는 분들께 도움이 될까 하여 올려봅니다! 🙂
빅데이터 연합동아리 투빅스에서 7주동안 텍스트 세미나를 진행했습니다. Stanford CS224n winter2019 : Natural Language Processing with Deep Learning 커리큘럼을 따라 진행하였으며, 블로그에 세미나 내용을 정리했습니다.
유명 자연어처리 강의인 CS224n을 공부하실 때, 저희 리뷰 블로그를 참고하시고 자연어처리 기본기를 다지는 데에 도움이 되셨으면 좋겠습니다! 감사합니다.
✔️ 2020 투빅스 텍스트 세미나 velog : https://velog.io/@tobigs-text1314/series
Tensorflow와 pytorch모두에서 사용이 가능합니다. 이제 한국어도 최신 언어모델을 기반으로 연구 하실 수 있으면 좋겠습니다. 간단한 예시를 colab으로 실행시켜 볼 수 있도록 만들어 두었습니다. 아직 training중인 모델은 업데이트가 될 예정이며, T5의 경우 아직 완전히 검증하진 못하였습니다. 현재 비공개 상태의 모델들도 추후 기회가 된다면 공개하도록 하겠습니다. 다양한 NLP 연구와 모델 사용에 대한 궁금증은 메세지로 남겨주십시오.
오늘 소개해 드릴 논문은 현재 많은 Image classification 분야에서 SOTA를 달성했던 논문인 Sharpness-Aware Minimization for Efficiently Improving Generalization 이라는 논문입니다. 오늘 논문 리뷰는 펀디멘탈팀의 이재윤님께서 진행해 주셨습니다.
발표자료 :https://www.slideshare.net/taeseonryu/sharpnessaware-minimization-for-efficiently-improving-generalization 발표된 논문 리스트 : https://github.com/Lilcob/-DL_PaperReadingMeeting
overparameterized 된 모델에서 training loss는 모델의 generalization 대한 보장을 하지 않고 있는게 현실 입니다. 실제로 일반적으로 수행되는 것처럼 training loss만 최적화하면 suboptimal 모델 quality로 이어집니다. 논문에서 소개하는 Sharpness-Aware Minimization (SAM)은 다양한 global minima 중에, 제너럴한 성능을 나타내는 글로벌 미니마를 찾아 성능향상에 기여를 하여 여러 태스크에서 기존의 모델링 방법에 SAM 방법론을 적용하여 다양한 분야에서 SOTA를 달성하였습니다.
 This is probably one of the biggest worries of those starting in the area of data science, learning/refreshing math
Image by DataSource.ai
Let’s be honest, most people didn’t do very well in math in school, maybe not even in college, and this is very scary and creates a barrier for those who want to explore this discipline called data science.
A few days ago I published a post inTowards Data Scienceand right here on our blog called“Study Plan for Learning Data Science Over the Next 12 Months”, where I gave some quarterly recommendations and made an emphasis on studying mathematics and statistics for this first quarter, and from which I received many questions about exactly which materials I recommended. Well, this post answers those questions. But before that, I want to give you a context.
Leaving aside the factors or reasons that have led most people to hate math, it is a reality that we need it in data science. For me, one of the biggest shortcomings I found in mathematics was its lack of applicability in the real world, I didn’t see a reason for intermediate and advanced mathematics, such as multivariate calculus. I confess that in school and college I didn’t like them for that reason, but I always did well and got good scores and averages above the majority (especially in statistics). But I still didn’t see how I could use a derivative or a matrix in the real world. I finally ended up as a software engineer and once I entered the world of data science I was able to make the connection between mathematics, statistics, and the real world.
On the other hand, it is important to clarify that we do not need a master’s degree in pure mathematics to do data science projects. As I mentioned in previous posts there is a big debate in the community about how much math we need to do a good job as data scientists.
We could say that data science is divided into two major fields of work: research and production
Byresearch, we mean the part of research and development, which normally takes place within a large company (usually a tech company), or which has focused on cutting-edge technological issues (such as medical research). Or it is also an area that is developed within universities. This sector has very limited job offers.
The great advantage is the deep knowledge of algorithms and their implementations, as well as being a person capable of creating variations of existing algorithms, to improve them. Or even create new machine learning algorithms.
The disadvantage is the unpractical nature of their work. It is a very theoretical work, in which often the only objective is to publish papers and is far from the business use cases in general. For reference on this,I recently read this post on Reddit, I recommend you read it.
Byproduction, we refer to the practical side of this discipline, where you’ll use generally and in your day to day job libraries such as scikit-learn, Tensorflow, Keras, Pytorch, and others. These libraries operate like a black box, where you enter data, you get an output, but you don’t know in detail what happened in the process. This also has its advantages and disadvantages, but it certainly makes life much easier when putting useful models into production. What I don’t recommend is to use them blindly, where you don’t have the minimum bases of mathematics to understand a little of their fundamentals and that is the objective of this post, to guide you and recommend you some valuable resources to have the necessary bases and not to operate blindly those libraries.
So if you decide to focus on Research and Development, you are going to need mathematics and statistics in depth (very in-depth). If you are going to go for the practical part, the libraries will help you deal with most of it, under the hood. It should be noted that most job offers are in the practical side.
Well, after the previous remarks, it is time to define which are the specific topics needed to have an initial basis in mathematics for data science.
Linear Algebra: This subject is important to have the fundamentals of working with data in vector and matrix form, to acquire skills to solve systems of linear algebraic equations, and to find the basic matrix decompositions and the general understanding of their applicability.
Calculus:Here it is important to study functional maps, limits (in case of sequences, functions of one and several variables), differentiation (from a single variable to multiple cases), integration, thus sequentially building a foundation for basic optimization. It is also important here to study gradient descent.
Probability theory: Here you should learn about random variables, i.e. a variable whose values are determined by a random experiment. Random variables are used as a model for the data generation processes we want to study. The properties of the data are deeply linked to the corresponding properties of the random variables, such as expected value, variance, and correlations.
Note: these subjects are much deeper than what I just mentioned, this is simply a guide of the subjects and resources recommended to approach mathematics in the field of data science.
Now that we have a better idea of the path we should take, let’s examine the recommended resources to address this topic. We will divide them into basic, intermediate, and advanced. In the advanced ones, we’ll have resources focused on deep learning
Basics: in this first section of resources we’ll recommend the mathematical basics. Mathematical thinking, algebra, and how to implement math with python.
1- Introduction to mathematical thinking
Price: Free
Image by Coursera
Description: Learn how to think the way mathematicians do — a powerful cognitive process developed over thousands of years.
Mathematical thinking is not the same as doing mathematics — at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box — a valuable ability in today’s world. This course helps to develop that crucial way of thinking.
2- Mathematical Foundation for AI and Machine Learning
Price: $46.99 usd
Image by Packt
Description: Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with innovations like self-driving cars, medical diagnosis and even beating humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.
Description: InMath for Programmersyou’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications.
Description: You can learn a lot of math with a bit of coding!
Many people don’t know that Python is a really powerful tool for learning math. Sure, you can use Python as a simple calculator, but did you know that Python can help you learn more advanced topics in algebra, calculus, and matrix analysis? That’s exactly what you’ll learn in this course.
This course is a perfect supplement to your school/university math course, or for your post-school return to mathematics.
Let me guess what you are thinking:
“But I don’t know Python!”That’s okay! This course is aimed at complete beginners; I take you through every step of the code. You don’t need to know anything about Python, although it’s useful if you already have some programming experience.
“But I’m not good at math!”You will be amazed at how much better you can learn math by using Python as a tool to help with your courses or your independent study. And that’s exactly the point of this course: Python programming as a tool to learn mathematics. This course is designed to be the perfect addition to any other math course or textbook that you are going through.
7- Introduction to Linear Models and Matrix Algebra
Price: Free
Image by Edx
Description: Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.
Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
Description: Python, one of the world’s most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.
The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You’ll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you’ll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you’ll discover Python’s applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, you’ll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Description: Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.
Each course of the specialisation ends with a project that gives an opportunity to see how the material of the course is used in Data Science. Each project is directed at solving practical problem in Data Science. In particular, in your projects you will analyse social graphs, predict estate prices and uncover hidden relations in the data.
Description: Discrete mathematics is a field of math that deals with studying finite and distinct elements. The theories and principles of discrete math are widely used in solving complexities and building algorithms in computer science and computing data in data science. It helps you to understand algorithms, binary, and general mathematics that is commonly used in data-driven tasks.
Learn Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up-to-speed with implementing discrete math principles to take your programming skills to another level. You’ll learn the discrete math language and methods crucial to studying and describing objects and functions in branches of computer science and machine learning. Complete with real-world examples, the book covers the internal workings of memory and CPUs, analyzes data for useful patterns, and shows you how to solve problems in network routing, encryption, and data science.
By the end of this book, you’ll have a deeper understanding of discrete mathematics and its applications in computer science, and get ready to work on real-world algorithm development and machine learning.
14- Math for Data Science and Machine Learning: University Level
Price: $12.99
Image by Udemy
Description: In this course we will learn math for data science and machine learning. We will also discuss the importance of Math for data science and machine learning in practical word. Moreover, Math for data science and machine learning course is bundle of two courses of linear algebra and probability and statistics. So, students will learn complete contents of probability and statistics and linear algebra. It is not like that you will not complete all the contents in this 7 hours videos course. This is a beautiful course and I have designed this course according to the need of the students.
Linear algebra and probability and statistics is usually offered for the students of data science, machine learning, python and IT students. So, that’s why I have prepared this dual course for different sciences.
I have taught this course multiple times on my universities classes. It is offered usually in two different modes like, it is offered as linear algebra for 100 marks paper and probability and statistics as another 100 marks paper for two different or in a same semesters. I usually focus on the method and examples while teaching this course. Examples clear the concepts of the students in a variety of way like, they can understand the main idea that instructor want to deliver if they feel typical the method of the subject or topics. So, focusing on example makes the course easy and understandable for the students.
Description: Data science courses contain math — no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.
Advanced: in this last section we will focus on the statistical part (probability theory) and the application of mathematics to deep learning algorithms.
Description: Inferential statistics allows us to draw conclusions from data that might not be immediately obvious. This course focuses on enhancing your ability to develop hypotheses and use common tests such as t-tests, ANOVA tests, and regression to validate your claims.
18- Statistical Methods and Applied Mathematics in Data Science
Price: $124.99
Image by Packt
Description: Machine learning and data analysis are the center of attraction for many engineers and scientists. The reason is quite obvious: its vast application in numerous fields and booming career options. And Python is one of the leading open source platforms for data science and numerical computing. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. If you are among those seeking to enhance their capabilities in machine learning, then this course is the right choice.
Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and focused recipes for data analysis and scientific computing. This course tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. You will apply state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. In short, you will be well versed with the standard methods in data science and mathematical modeling.
19- Exploring Math for Programmers and Data Scientists
Price: Free
Image by Manning
Description:Exploring Math for Programmers and Data Scientistsshowcases chapters from three Manning books, chosen by author and master-of-math Paul Orland. You’ll start with a look at the nearest neighbor search problem, common with multidimensional data, and walk through a real-world solution for tackling it. Next, you’ll delve into a set of methods and techniques integral to Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval. In the last chapter, you’ll work with digital audio data, using mathematical functions in different and interesting ways. Begin sharpening your competitive edge with the fun and fascinating math in this (free!) practical guide!
Description: Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.
You’ll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.
By the end of this book, you’ll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
Description:Math and Architectures of Deep Learningsets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.
When we have limited time for study, we should select those that we feel best and those that fit our style. For example, you might prefer videos about books, so go ahead and choose what suits you best. This material is sufficient whether you want to take a brief look at the mathematics, or if you want to go deeper into it. I hope you find it useful.
If you have other recommendations for courses, books or videos, please leave them in the comments so that we can all create links of interest.
Note: we are building a private community in Slack of data scientist, if you want to join us you can register here:https://www.datasource.ai/en#slack
I hope you enjoyed this reading! you can follow me ontwitterorlinkedin
