Discovering Diverse Athletic Jumping Strategies
In this paper, the researchers present a «smart» framework to discover motion strategies for such athletic skills as the high jump. It allows us to come up with, explore, and optimize a wide range of novel motion strategies for jumpers through a sample-efficient Bayesian diversity search (BDS) algorithm.
WWW — https://bit.ly/3yiDPdD
Paper — https://bit.ly/3wjnC6p
Video — https://bit.ly/33SAi82 & https://bit.ly/3hxY8hm
Code — https://bit.ly/2QpVNKc
In this paper, the researchers present a «smart» framework to discover motion strategies for such athletic skills as the high jump. It allows us to come up with, explore, and optimize a wide range of novel motion strategies for jumpers through a sample-efficient Bayesian diversity search (BDS) algorithm.
WWW — https://bit.ly/3yiDPdD
Paper — https://bit.ly/3wjnC6p
Video — https://bit.ly/33SAi82 & https://bit.ly/3hxY8hm
Code — https://bit.ly/2QpVNKc
Data Science Digest — 19.05.21
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, event materials, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3hxDbTQ
Join 👉 @DataScienceDigest
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, event materials, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3hxDbTQ
Join 👉 @DataScienceDigest
Detecting Deforestation from Satellite Images
Deforestation is a global issue that needs to be addressed on a larger scale. Fortunately, AI can help us detect areas suffering from deforestation much faster. In this article, you’ll learn about a full stack deep learning project that uses high-resolution from the Amazon rainforest to build and train a 95% accurate model that detects areas with loss of trees from space.
https://bit.ly/3frkJcQ
Deforestation is a global issue that needs to be addressed on a larger scale. Fortunately, AI can help us detect areas suffering from deforestation much faster. In this article, you’ll learn about a full stack deep learning project that uses high-resolution from the Amazon rainforest to build and train a 95% accurate model that detects areas with loss of trees from space.
https://bit.ly/3frkJcQ
Build a System to Identify Fake News Articles
Fake news is a false or misleading content presented as news in different formats. Fake news is considered to be a huge problem, since it erodes trust in any content (even from respectable sources). Thankfully, AI can help identify fake news, and in this article, we’ll look at how to apply text analytics and classical machine learning for that.
https://bit.ly/3v9seM2
Fake news is a false or misleading content presented as news in different formats. Fake news is considered to be a huge problem, since it erodes trust in any content (even from respectable sources). Thankfully, AI can help identify fake news, and in this article, we’ll look at how to apply text analytics and classical machine learning for that.
https://bit.ly/3v9seM2
PyCaret 101 — For Beginners
PyCaret is an open-source ML library and end-to-end model management tool for ML workflows automation. It can replace voluminous code with just a few lines, which only adds to its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end machine learning pipelines. In this article, you’ll learn how to get started with PyCaret.
https://bit.ly/2SkBgqX
PyCaret is an open-source ML library and end-to-end model management tool for ML workflows automation. It can replace voluminous code with just a few lines, which only adds to its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end machine learning pipelines. In this article, you’ll learn how to get started with PyCaret.
https://bit.ly/2SkBgqX
DataScience Digest is up and running thanks to the awesome community, you guys. Your interest, your feedback, and your donations keep us going. Kindly support us on Patreon to get more news and updates on what’s going on in the world of Data Science.
https://bit.ly/3vfCnqw
https://bit.ly/3vfCnqw
Time Series Anomaly Detection with PyCaret
In this step-by-step, beginner-friendly tutorial, you’ll learn how you can use PyCaret’s Unsupervised Anomaly Detection Module to detect anomalies in time series data. We’ll start with the basics of anomaly detection and move forward to training and evaluating the anomaly detection model using PyCaret, to labeling anomalies and analyzing the results.
https://bit.ly/3yBcELA
In this step-by-step, beginner-friendly tutorial, you’ll learn how you can use PyCaret’s Unsupervised Anomaly Detection Module to detect anomalies in time series data. We’ll start with the basics of anomaly detection and move forward to training and evaluating the anomaly detection model using PyCaret, to labeling anomalies and analyzing the results.
https://bit.ly/3yBcELA
NVIDIA Deepstream Quickstart
NVIDIA has been working hard to improve their deep learning stack. In this article, you’ll learn how to give it a try in a controlled environment. Specifically, you’ll set up a docker container to run an NVIDIA deepstream pipeline on a GPU or a Jetson. The article can serve as a point of reference for beginners looking to make pipelines with NVIDIA.
https://bit.ly/3hO6DFl
Support us on Patreon 👉 https://bit.ly/2SjsNEK
NVIDIA has been working hard to improve their deep learning stack. In this article, you’ll learn how to give it a try in a controlled environment. Specifically, you’ll set up a docker container to run an NVIDIA deepstream pipeline on a GPU or a Jetson. The article can serve as a point of reference for beginners looking to make pipelines with NVIDIA.
https://bit.ly/3hO6DFl
Support us on Patreon 👉 https://bit.ly/2SjsNEK
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
In this paper, Dong Chen et al. formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem. They propose an efficient MARL framework that can be used in dynamic traffic. A novel safety supervisor is developed to significantly reduce collision rate and greatly expedite the training process.
Paper — https://bit.ly/2QSyPvE
Code — https://bit.ly/34lLJoN
Code — https://bit.ly/2SnRiRd
In this paper, Dong Chen et al. formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem. They propose an efficient MARL framework that can be used in dynamic traffic. A novel safety supervisor is developed to significantly reduce collision rate and greatly expedite the training process.
Paper — https://bit.ly/2QSyPvE
Code — https://bit.ly/34lLJoN
Code — https://bit.ly/2SnRiRd
Data Science Digest — 26.05.21
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, event materials, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3hW5agi
Join 👉 @DataScienceDigest
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, event materials, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3hW5agi
Join 👉 @DataScienceDigest
Easy MLOps with PyCaret + MLflow
PyCaret is an open-source, low-code library for machine learning. Built on Python, it’s simple and easy to use, and allows you to quickly and efficiently handle ML models. MLflow is an open-source platform to manage the ML lifecycle. In this article, you’ll learn how to integrate MLOps in your ML experiments using PyCaret and MLflow.
https://bit.ly/3yJmomP
PyCaret is an open-source, low-code library for machine learning. Built on Python, it’s simple and easy to use, and allows you to quickly and efficiently handle ML models. MLflow is an open-source platform to manage the ML lifecycle. In this article, you’ll learn how to integrate MLOps in your ML experiments using PyCaret and MLflow.
https://bit.ly/3yJmomP
Lessons on ML Platforms — From Netflix, DoorDash, Spotify, and More
In this article, the author draws from the experience of AI industry leaders to answer the ubiquitous question, How can organizations enable data scientists to repeatedly deliver value, out of scope of the existing ML production systems? Here he also looks into best practices, tools, and management approaches to resolve the value delivery problem.
https://bit.ly/3uA8Hna
In this article, the author draws from the experience of AI industry leaders to answer the ubiquitous question, How can organizations enable data scientists to repeatedly deliver value, out of scope of the existing ML production systems? Here he also looks into best practices, tools, and management approaches to resolve the value delivery problem.
https://bit.ly/3uA8Hna
Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
In this paper, researchers look into fairness and bias issues in Twitter’s automated image cropping system. They found systematic disparities in cropping, identified contributing factors, and to resolve the problem proposed the removal of saliency-based cropping in favor of a solution that better preserves user agency.
Paper — https://bit.ly/3yM4ksa
Code —https://bit.ly/3fSifUX
In this paper, researchers look into fairness and bias issues in Twitter’s automated image cropping system. They found systematic disparities in cropping, identified contributing factors, and to resolve the problem proposed the removal of saliency-based cropping in favor of a solution that better preserves user agency.
Paper — https://bit.ly/3yM4ksa
Code —https://bit.ly/3fSifUX
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
In this paper, the team of researchers propose a linearly-assembled pixel-adaptive regression network (LAPAR), designed and built to deal with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. LAPAR is highly lightweight and easy to optimize, and helps achieve superb results on SISR benchmarks.
Paper — https://bit.ly/3yYFzcC
Code — https://bit.ly/2RPlPaG
In this paper, the team of researchers propose a linearly-assembled pixel-adaptive regression network (LAPAR), designed and built to deal with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. LAPAR is highly lightweight and easy to optimize, and helps achieve superb results on SISR benchmarks.
Paper — https://bit.ly/3yYFzcC
Code — https://bit.ly/2RPlPaG
GAN Prior Embedded Network for Blind Face Restoration in the Wild
In this paper, Tao Yang et al. use existing generative adversarial network-based methods to solve the problem of blind face restoration from severely degraded face images in the wild. The proposed GAN prior embedded network (GPEN) generates visually photo-realistic results, which are significantly superior to BFR methods both quantitatively and qualitatively.
Paper — https://bit.ly/3fCV8PA
Code — https://bit.ly/2SIt04e
In this paper, Tao Yang et al. use existing generative adversarial network-based methods to solve the problem of blind face restoration from severely degraded face images in the wild. The proposed GAN prior embedded network (GPEN) generates visually photo-realistic results, which are significantly superior to BFR methods both quantitatively and qualitatively.
Paper — https://bit.ly/3fCV8PA
Code — https://bit.ly/2SIt04e
Build a Scalable Machine Learning Pipeline for Ultra-High Resolution Medical Images using Amazon SageMaker
In this comprehensive article by the AWS team, you’ll learn how to preprocess medical images in ultra-high resolution, train an image classifier on these preprocessed images, and deploy a pretrained model as an API — all done on the Amazon SageMaker platform — to, finally, build a highly scalable machine learning pipeline.
https://amzn.to/3fAJowT
In this comprehensive article by the AWS team, you’ll learn how to preprocess medical images in ultra-high resolution, train an image classifier on these preprocessed images, and deploy a pretrained model as an API — all done on the Amazon SageMaker platform — to, finally, build a highly scalable machine learning pipeline.
https://amzn.to/3fAJowT
Albumentations 1.0.0 has been released!
Albumentations is a computer vision tool and a Python library designed to improve the performance of deep convolutional neural networks by enabling fast, flexible, cost- and resource-efficient image augmentations. The tool can be used for different CV tasks, including object classification, segmentation, and detection.
New version contains 10 new transforms, independence from imgaug, bug fixes, etc.
https://bit.ly/3fKM6jC
Albumentations is a computer vision tool and a Python library designed to improve the performance of deep convolutional neural networks by enabling fast, flexible, cost- and resource-efficient image augmentations. The tool can be used for different CV tasks, including object classification, segmentation, and detection.
New version contains 10 new transforms, independence from imgaug, bug fixes, etc.
https://bit.ly/3fKM6jC
Data Science Digest — 02.06.21
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, datasets, videos, and tools on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3vN2CF4
Join 👉@DataScienceDigest
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, datasets, videos, and tools on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3vN2CF4
Join 👉@DataScienceDigest