PyCon US 2021 [Conference Materials]
This playlist features all keynotes, talks, and other materials from PyCon US 2021, a virtual conference for the community using and developing the open-source Python programming language. Over 80 videos in total!
https://bit.ly/2TsBJIi
This playlist features all keynotes, talks, and other materials from PyCon US 2021, a virtual conference for the community using and developing the open-source Python programming language. Over 80 videos in total!
https://bit.ly/2TsBJIi
Session-based Recommender Systems
In this extensive research report by Cloudera Fast Forward, you’ll learn all the ins and outs of designing, building, and managing AI/ML-powered recommender systems. The authors will demonstrate how to use specific algorithms and datasets to arrive at conclusions about the do’s and don’ts of building such systems (e.g. while using word2vec).
https://bit.ly/3pS8URC
In this extensive research report by Cloudera Fast Forward, you’ll learn all the ins and outs of designing, building, and managing AI/ML-powered recommender systems. The authors will demonstrate how to use specific algorithms and datasets to arrive at conclusions about the do’s and don’ts of building such systems (e.g. while using word2vec).
https://bit.ly/3pS8URC
Dynamically Generating DAGs in Airflow
In this guide, the Astronomer team looks into specific methods of dynamically generating DAGs in Airflow, from single-file methods to multiple-file methods. Every method is accompanied by code and examples. The team also presents DAG Factory, an open source Python library for dynamically generating Airflow DAGs from YAML files.
https://bit.ly/3xt8RhF
In this guide, the Astronomer team looks into specific methods of dynamically generating DAGs in Airflow, from single-file methods to multiple-file methods. Every method is accompanied by code and examples. The team also presents DAG Factory, an open source Python library for dynamically generating Airflow DAGs from YAML files.
https://bit.ly/3xt8RhF
Data Science Digest — 17.06.21
The new issue of DataScienceDigest is here! Facebook AI migrates its systems to PyTorch, metric learning tips & tricks, session-based recommender systems, AndroidEnv, materials from PyCon US 2021, and more…
https://bit.ly/3vshrMs
Join 👉@DataScienceDigest
The new issue of DataScienceDigest is here! Facebook AI migrates its systems to PyTorch, metric learning tips & tricks, session-based recommender systems, AndroidEnv, materials from PyCon US 2021, and more…
https://bit.ly/3vshrMs
Join 👉@DataScienceDigest
Data Phoenix pinned «Data Science Digest — 17.06.21 The new issue of DataScienceDigest is here! Facebook AI migrates its systems to PyTorch, metric learning tips & tricks, session-based recommender systems, AndroidEnv, materials from PyCon US 2021, and more… https://bit.ly/3vshrMs…»
Mathematics for Machine Learning
«Mathematics for Machine Learning» by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong brings the mathematical foundations of basic ML concepts to all those who struggle with the mathematical knowledge required to read an ML textbook. This book is intended to be a guidebook to the vast mathematical literature that forms the foundations of modern machine learning.
https://bit.ly/3gDHfkG
«Mathematics for Machine Learning» by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong brings the mathematical foundations of basic ML concepts to all those who struggle with the mathematical knowledge required to read an ML textbook. This book is intended to be a guidebook to the vast mathematical literature that forms the foundations of modern machine learning.
https://bit.ly/3gDHfkG
Conversational AI
In this video, Merve Noyan, Google Developer Expert on Machine Learning, gives an overview of the conversational AI niche. The talk is hosted by Alexey Grigorev, the founder of DataTalks.Club.
https://bit.ly/35ESW4d
In this video, Merve Noyan, Google Developer Expert on Machine Learning, gives an overview of the conversational AI niche. The talk is hosted by Alexey Grigorev, the founder of DataTalks.Club.
https://bit.ly/35ESW4d
YouTube
Conversational AI - Merve Noyan
Start from 15:45 (YouTube doesn't let me cut this video)
Links:
- Merve's Twitter: https://twitter.com/mervenoyann
- Merve's podcast: https://www.youtube.com/channel/UCU-KsNFmnZ_v3RZ7MKvCzDg
- cs224n course: http://web.stanford.edu/class/cs224n/
- Rasa…
Links:
- Merve's Twitter: https://twitter.com/mervenoyann
- Merve's podcast: https://www.youtube.com/channel/UCU-KsNFmnZ_v3RZ7MKvCzDg
- cs224n course: http://web.stanford.edu/class/cs224n/
- Rasa…
Deep Learning Do It Yourself!
The website is a collection of more than 20 modules on learning deep learning. As a student, you can walk through the modules at your own pace and interact with others. You can also contribute to the materials by adding new modules yourself.
https://bit.ly/3vMub0l
#DataScienceDigest #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #deeplearning
Subscribe to our weekly newsletter — https://bit.ly/3cS4G7y
The website is a collection of more than 20 modules on learning deep learning. As a student, you can walk through the modules at your own pace and interact with others. You can also contribute to the materials by adding new modules yourself.
https://bit.ly/3vMub0l
#DataScienceDigest #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #deeplearning
Subscribe to our weekly newsletter — https://bit.ly/3cS4G7y
Comparing Test Sets with Item Response Theory
In this paper, Clara Vania et al. use the Item Response Theory to evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models.
https://bit.ly/3xHDkJj
In this paper, Clara Vania et al. use the Item Response Theory to evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models.
https://bit.ly/3xHDkJj
How Airbnb Standardized Metric Computation at Scale
The engineering team of Airbnb reveals the design principles of Minerva compute infrastructure. Minerva is a single source of truth metric platform that standardizes the way business metrics are created, computed, served, and consumed. The article features the link to the first post on Minerva. Check it out, too!
https://bit.ly/3zLDv8g
The engineering team of Airbnb reveals the design principles of Minerva compute infrastructure. Minerva is a single source of truth metric platform that standardizes the way business metrics are created, computed, served, and consumed. The article features the link to the first post on Minerva. Check it out, too!
https://bit.ly/3zLDv8g
Medium
How Airbnb Standardized Metric Computation at Scale
Part II: The six design principles of Minerva compute infrastructure
AI Can Now Emulate Text Style in Images in One Shot — Using Just a Single Word
In this article, the engineering team of Facebook AI presents TextStyleBrush, an AI research project that can copy the style of text in a photo using just a single word. With this AI model, you can edit and replace text in images. The team hopes to spur dialogue and research into detecting potential misuse of this type of technology, so make sure to contribute.
https://bit.ly/3wU3t7I
In this article, the engineering team of Facebook AI presents TextStyleBrush, an AI research project that can copy the style of text in a photo using just a single word. With this AI model, you can edit and replace text in images. The team hopes to spur dialogue and research into detecting potential misuse of this type of technology, so make sure to contribute.
https://bit.ly/3wU3t7I
Facebook
AI can now emulate text style in images in one shot — using just a single word
Today, we’re introducing TextStyleBrush, the first self-supervised AI model that replaces text in existing images of both scenes and handwriting — in one shot — using just a single example word.
Data Science Digest — 24.06.21
The new issue of DataScienceDigest is here! The impact of NLP and the growing budgets to drive AI transformations. How Airbnb standardized metric computation at scale. Cross-Validation, MASA-SR, AgileGAN, EfficientNetV2, and more…
https://bit.ly/3qnuy0u
Join 👉@DataScienceDigest
The new issue of DataScienceDigest is here! The impact of NLP and the growing budgets to drive AI transformations. How Airbnb standardized metric computation at scale. Cross-Validation, MASA-SR, AgileGAN, EfficientNetV2, and more…
https://bit.ly/3qnuy0u
Join 👉@DataScienceDigest
Ingestion and Historization in the Data Lake
In this video, Alexey Grigorev, the founder of DataTalks.Club, hosts Illia Todor, Data Engineer, to talk about ingestion and historization of data in the data lake.
https://bit.ly/3zWKaMP
In this video, Alexey Grigorev, the founder of DataTalks.Club, hosts Illia Todor, Data Engineer, to talk about ingestion and historization of data in the data lake.
https://bit.ly/3zWKaMP
YouTube
Ingestion and Historization in the Data Lake - Illia Todor
Links
- Slides: https://www.slideshare.net/IlyaTodor/ingestion-and-historization-in-the-data-lake
- Feedback form: https://forms.gle/YByeD5GKsLuiVQEF8
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html…
- Slides: https://www.slideshare.net/IlyaTodor/ingestion-and-historization-in-the-data-lake
- Feedback form: https://forms.gle/YByeD5GKsLuiVQEF8
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html…
MLOps Toys
The platform is a collection of MLOps projects by category, including data versioning, training orchestration, feature store, experiment tracking, model serving, model monitoring, and explainability.
https://bit.ly/3xV97GF
The platform is a collection of MLOps projects by category, including data versioning, training orchestration, feature store, experiment tracking, model serving, model monitoring, and explainability.
https://bit.ly/3xV97GF
mlops.toys
MLOps Toys | A Curated List of Machine Learning Projects
Check out this curated list of the most useful MLOps tools, projects and more. Have something to add? Let us know!
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
In this research, Yongming Rao et al. propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input. A lightweight prediction module can estimate the importance score of each token given the current features. The module is added to different layers to prune redundant tokens hierarchically.
Web Page — https://bit.ly/3dgESlq
Paper — https://bit.ly/3xWf5XJ
Code — https://bit.ly/3jlUtUK
In this research, Yongming Rao et al. propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input. A lightweight prediction module can estimate the importance score of each token given the current features. The module is added to different layers to prune redundant tokens hierarchically.
Web Page — https://bit.ly/3dgESlq
Paper — https://bit.ly/3xWf5XJ
Code — https://bit.ly/3jlUtUK
dynamicvit.ivg-research.xyz
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Yongming Rao1 Wenliang Zhao1 Benlin Liu2
Jiwen Lu1 Jie Zhou1 Cho-Jui Hsieh2
1Tsinghua University 2University of California, Los Angeles
[Paper (arXiv)] …
Yongming Rao1 Wenliang Zhao1 Benlin Liu2
Jiwen Lu1 Jie Zhou1 Cho-Jui Hsieh2
1Tsinghua University 2University of California, Los Angeles
[Paper (arXiv)] …
Consistent Instance False Positive Improves Fairness in Face Recognition
In this paper, Xingkun Xu et al. propose a false positive rate penalty loss, a novel method to mitigate face recognition bias by increasing the consistency of instance False Positive Rate (FPR). The method requires no demographic annotations, allowing to mitigate bias among demographic groups divided by various attributes.
Paper — https://bit.ly/361fHiQ
Code — https://bit.ly/2UKAqoF
In this paper, Xingkun Xu et al. propose a false positive rate penalty loss, a novel method to mitigate face recognition bias by increasing the consistency of instance False Positive Rate (FPR). The method requires no demographic annotations, allowing to mitigate bias among demographic groups divided by various attributes.
Paper — https://bit.ly/361fHiQ
Code — https://bit.ly/2UKAqoF
The FLORES-101 Data Set: Helping Build Better Translation Systems Around the World
Building on the success of machine translation systems like M2M-100, Facebook AI has open-sourced FLORES-101, a many-to-many evaluation data set covering 101 languages from all over the world, to enable researchers to rapidly test and improve upon multilingual translation models like M2M-100. In this article, you’ll delve into its basics.
https://bit.ly/3hja98z
Building on the success of machine translation systems like M2M-100, Facebook AI has open-sourced FLORES-101, a many-to-many evaluation data set covering 101 languages from all over the world, to enable researchers to rapidly test and improve upon multilingual translation models like M2M-100. In this article, you’ll delve into its basics.
https://bit.ly/3hja98z
Facebook
The FLORES-101 data set: Helping build better translation systems around the world
Today we are open-sourcing FLORES-101, a first-of-its-kind, many-to-many evaluation data set covering 101 languages (10,100 translation directions) from all over the world.
Multivariate Probabilistic Regression with Natural Gradient Boosting
Natural Gradient Boosting (NGBoost) is a new method proposed by the researchers. It is based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. The method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
Paper — https://bit.ly/3haEF5C
Code — https://bit.ly/3qC1SB3
Natural Gradient Boosting (NGBoost) is a new method proposed by the researchers. It is based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. The method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
Paper — https://bit.ly/3haEF5C
Code — https://bit.ly/3qC1SB3