Ultimate Machine Learning Cheat Sheet
Notes on top-level topics from Stanford's CS 229 by Shervine Amidi and Afshine Amidi:
* Supervised learning
* Unsupervised learning
* Deep learning
* Tips and tricks
* Probability and stats refresher
* Algebra and calculus refresher
Forward this message to your Saved Messages to make sure, you won’t lose it.
Repo link: https://github.com/afshinea/stanford-cs-229-machine-learning
#Stanford #cheatsheet
Notes on top-level topics from Stanford's CS 229 by Shervine Amidi and Afshine Amidi:
* Supervised learning
* Unsupervised learning
* Deep learning
* Tips and tricks
* Probability and stats refresher
* Algebra and calculus refresher
Forward this message to your Saved Messages to make sure, you won’t lose it.
Repo link: https://github.com/afshinea/stanford-cs-229-machine-learning
#Stanford #cheatsheet
GitHub
GitHub - afshinea/stanford-cs-229-machine-learning: VIP cheatsheets for Stanford's CS 229 Machine Learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning
Deep learning cheatsheets, covering content of Stanford’s CS 230 class.
CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
#cheatsheet #Stanford #dl #cnn #rnn #tipsntricks
CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
#cheatsheet #Stanford #dl #cnn #rnn #tipsntricks
stanford.edu
CS 230 - Convolutional Neural Networks Cheatsheet
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
From abstract: The self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in.
This is an article about chatbot which is capable of true online learning. There is also a venturebeat article on the subject, covering the perspective: «Facebook and Stanford researchers design a chatbot that learns from its mistakes».
Venturebeat: https://venturebeat.com/2019/01/17/facebook-and-stanford-researchers-design-a-chatbot-that-learns-from-its-mistakes/
ArXiV: https://arxiv.org/abs/1901.05415
#NLP #chatbot #facebook #Stanford
From abstract: The self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in.
This is an article about chatbot which is capable of true online learning. There is also a venturebeat article on the subject, covering the perspective: «Facebook and Stanford researchers design a chatbot that learns from its mistakes».
Venturebeat: https://venturebeat.com/2019/01/17/facebook-and-stanford-researchers-design-a-chatbot-that-learns-from-its-mistakes/
ArXiV: https://arxiv.org/abs/1901.05415
#NLP #chatbot #facebook #Stanford
VentureBeat
Facebook and Stanford researchers design a chatbot that learns from its mistakes
In a new paper, scientists at Facebook AI Research and Stanford describe a chatbot that learns from its mistakes over time.
StanfordNLP release.
New Python #NLProc package. StanfordNLP provides native, neural (PyTorch) tokenization, POS tagging and dependency parsing for 53 languages based on UD v2 — and a Python CoreNLP interface.
Pip:
PyPI: https://pypi.org/project/stanfordnlp/
Link: https://stanfordnlp.github.io/stanfordnlp/
#NLP #Stanford
New Python #NLProc package. StanfordNLP provides native, neural (PyTorch) tokenization, POS tagging and dependency parsing for 53 languages based on UD v2 — and a Python CoreNLP interface.
Pip:
pip install stanfordnlpPyPI: https://pypi.org/project/stanfordnlp/
Link: https://stanfordnlp.github.io/stanfordnlp/
#NLP #Stanford
PyPI
stanfordnlp
Official Stanford NLP Python Library
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: http://go.nature.com/2LSMaAz
#LungCancer #Cancer #biolearning #healthcare #DL #CV
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: http://go.nature.com/2LSMaAz
#LungCancer #Cancer #biolearning #healthcare #DL #CV
Set of animated Artificial Intelligence cheatsheets covering Stanford's CS 221 class:
Reflex-based: http://stanford.io/2EqNPHy
States-based: http://stanford.io/2wh4F7u
Variables-based: http://stanford.io/2HAiAfh
Logic-based: http://stanford.io/2M7taia
GitHub: https://github.com/afshinea/stanford-cs-221-artificial-intelligence
#cheatsheet #Stanford
Reflex-based: http://stanford.io/2EqNPHy
States-based: http://stanford.io/2wh4F7u
Variables-based: http://stanford.io/2HAiAfh
Logic-based: http://stanford.io/2M7taia
GitHub: https://github.com/afshinea/stanford-cs-221-artificial-intelligence
#cheatsheet #Stanford
🎓CS224W: Machine Learning with Graphs
Great course from #Stanford. You still on time to jump at studying from one of the best schools.
Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections.
Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.
Link: http://cs224w.stanford.edu
Videos link: http://snap.stanford.edu/class/cs224w-videos-2019/
#MOOC #entrylevel #wheretostart
Great course from #Stanford. You still on time to jump at studying from one of the best schools.
Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections.
Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.
Link: http://cs224w.stanford.edu
Videos link: http://snap.stanford.edu/class/cs224w-videos-2019/
#MOOC #entrylevel #wheretostart
Free eBook from Stanford: Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Base material you need to understand how neural networks and other #ML algorithms work.
Link: https://web.stanford.edu/~boyd/vmls/
#Stanford #MOOC #WhereToStart #free #ebook #algebra #linalg #NN
Base material you need to understand how neural networks and other #ML algorithms work.
Link: https://web.stanford.edu/~boyd/vmls/
#Stanford #MOOC #WhereToStart #free #ebook #algebra #linalg #NN
Driverless DeLorean drifting
#Stanford researchers taught autonomous car driving AI to drift to handle hazardous conditions better.
Link: https://news.stanford.edu/2019/12/20/autonomous-delorean-drives-sideways-move-forward/
#Autonomous #selfdriving #RL #CV #DL #DeLorean
#Stanford researchers taught autonomous car driving AI to drift to handle hazardous conditions better.
Link: https://news.stanford.edu/2019/12/20/autonomous-delorean-drives-sideways-move-forward/
#Autonomous #selfdriving #RL #CV #DL #DeLorean
SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis
#SberQuAD – a large scale analog of #Stanford #SQuAD in the Russian language – is a valuable resource that has not been properly presented to the scientific community.
SberQuAD creators generally followed a procedure described by the SQuAD authors, which resulted in the similarly high lexical overlap between questions and sentences with answers.
paper: https://arxiv.org/abs/1912.09723
link to SDSJ Task B dataset: http://files.deeppavlov.ai/datasets/sber_squad-v1.1.tar.gz
#SberQuAD – a large scale analog of #Stanford #SQuAD in the Russian language – is a valuable resource that has not been properly presented to the scientific community.
SberQuAD creators generally followed a procedure described by the SQuAD authors, which resulted in the similarly high lexical overlap between questions and sentences with answers.
paper: https://arxiv.org/abs/1912.09723
link to SDSJ Task B dataset: http://files.deeppavlov.ai/datasets/sber_squad-v1.1.tar.gz
Stanford updated tool Stanza with #NER for biomedical and clinical terms
Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.
That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.
Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640
#NLProc #NLU #Stanford #biolearning #medicallearning
Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.
That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.
Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640
#NLProc #NLU #Stanford #biolearning #medicallearning
