Motion Representations for Articulated Animation
In this research, Aliaksandr Siarohin et al. present novel motion representations for animating articulated objects consisting of distinct parts. Learn about the new method they propose, how it differs from keypoint-based works, and how it can be used to animate a variety of objects, surpassing previous methods on existing benchmarks.
Paper — https://bit.ly/3eVsVlk
Code — https://bit.ly/33q5nj4
Video — https://bit.ly/3tmvlOZ
In this research, Aliaksandr Siarohin et al. present novel motion representations for animating articulated objects consisting of distinct parts. Learn about the new method they propose, how it differs from keypoint-based works, and how it can be used to animate a variety of objects, surpassing previous methods on existing benchmarks.
Paper — https://bit.ly/3eVsVlk
Code — https://bit.ly/33q5nj4
Video — https://bit.ly/3tmvlOZ
How to Plot XGBoost Trees in R
XGBoost is a popular ML algorithm, which is frequently used in Kaggle competitions and has many practical use cases. If you always wanted to learn more about XGBoost, this short tutorial is for you. You will learn how to prepare the dataset for modeling, train the XGBoot model, plot the XGBoot trees, then export tree plots, and plot multiple trees at once.
https://bit.ly/33pYiiv
XGBoost is a popular ML algorithm, which is frequently used in Kaggle competitions and has many practical use cases. If you always wanted to learn more about XGBoost, this short tutorial is for you. You will learn how to prepare the dataset for modeling, train the XGBoot model, plot the XGBoot trees, then export tree plots, and plot multiple trees at once.
https://bit.ly/33pYiiv
Multiple Time Series Forecasting with PyCaret
PyCaret is a popular machine learning library and a model management tool for automating machine learning workflows. It allows us to build and deploy end-to-end ML prototypes quickly and efficiently. In this step-by-step tutorial, you will learn how to use PyCaret to forecast multiple time series in less than 50 lines of code.
https://bit.ly/3xWy2KO
PyCaret is a popular machine learning library and a model management tool for automating machine learning workflows. It allows us to build and deploy end-to-end ML prototypes quickly and efficiently. In this step-by-step tutorial, you will learn how to use PyCaret to forecast multiple time series in less than 50 lines of code.
https://bit.ly/3xWy2KO
AutoNLP: Automatic Text Classification with SOTA Models
Developing NLP models can be challenging as you need to account for multiple factors, including model selection, data preprocessing, training, optimization, and infrastructure. AutoNLP, a tool to automate the end-to-end life cycle of an NLP model, can make this process much easier. Learn how to use AutoNLP in this step-by-step guide.
https://bit.ly/3xYkyhr
Developing NLP models can be challenging as you need to account for multiple factors, including model selection, data preprocessing, training, optimization, and infrastructure. AutoNLP, a tool to automate the end-to-end life cycle of an NLP model, can make this process much easier. Learn how to use AutoNLP in this step-by-step guide.
https://bit.ly/3xYkyhr
What Is Face Recognition?
In this 101 tutorial, Adrian Rosebrock of the PyImageSearch team explains everything you need to know about face recognition, from what it is and how it works to how it is different from face detection and advanced face recognition algorithms you can start using today.
https://bit.ly/33y5qJQ
In this 101 tutorial, Adrian Rosebrock of the PyImageSearch team explains everything you need to know about face recognition, from what it is and how it works to how it is different from face detection and advanced face recognition algorithms you can start using today.
https://bit.ly/33y5qJQ
Probabilistic Machine Learning Course by Philipp Hennig
The course by Philipp Hennig at the University of Tübingen covers the probabilistic paradigm for machine learning, and occasionally draws direct connections to statistical and deep learning. The course is aimed at master students in computer science and related fields.
https://bit.ly/3hkxuIU
The course by Philipp Hennig at the University of Tübingen covers the probabilistic paradigm for machine learning, and occasionally draws direct connections to statistical and deep learning. The course is aimed at master students in computer science and related fields.
https://bit.ly/3hkxuIU
Data Science Digest — 13.05.21
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, courses, podcasts, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3oo1K7f
Join 👉 @DataScienceDigest
The new issue of DataScienceDigest is here! Hop to learn about the latest news, articles, tutorials, research papers, courses, podcasts, and projects on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
https://bit.ly/3oo1K7f
Join 👉 @DataScienceDigest
Animating Pictures with Eulerian Motion Fields
In this paper, Aleksander Holynski et al. demonstrate a fully automatic method for converting a still image into a realistic animated looping video. The images are animated using a deep warping technique: pixels are encoded as deep features, features are warped via Eulerian motion, and the warped feature maps are decoded as images.
WWW — https://bit.ly/3uMSJH6
Paper — https://bit.ly/33GTup5
Video —https://bit.ly/3tOLPQa
Code — coming soon
In this paper, Aleksander Holynski et al. demonstrate a fully automatic method for converting a still image into a realistic animated looping video. The images are animated using a deep warping technique: pixels are encoded as deep features, features are warped via Eulerian motion, and the warped feature maps are decoded as images.
WWW — https://bit.ly/3uMSJH6
Paper — https://bit.ly/33GTup5
Video —https://bit.ly/3tOLPQa
Code — coming soon
Data Fest returns! 🎉 And pretty soon
📅 Starting May 22nd and until June 19th, we host an Online Fest just like we did last year:
🔸Our YouTube live stream returns to a zoo-forest with 🦙🦌 and this time 🐻a bear cub! (RU)
🔸Unlimited networking in our spatial.chat - May 22nd will be the real community maelstrom (RU & EN)
🔸Tracks on our ODS.AI platform, with new types of activities and tons of new features (RU & EN)
Registration is live! Check out Data Fest 2021 website for the astonishing tracks we have in our program and all the details. 🤩
https://bit.ly/3tNeaX9
📅 Starting May 22nd and until June 19th, we host an Online Fest just like we did last year:
🔸Our YouTube live stream returns to a zoo-forest with 🦙🦌 and this time 🐻a bear cub! (RU)
🔸Unlimited networking in our spatial.chat - May 22nd will be the real community maelstrom (RU & EN)
🔸Tracks on our ODS.AI platform, with new types of activities and tons of new features (RU & EN)
Registration is live! Check out Data Fest 2021 website for the astonishing tracks we have in our program and all the details. 🤩
https://bit.ly/3tNeaX9
Data Scientist vs Machine Learning Engineer Skills. Here’s the Difference.
Data Science and Machine Learning have become buzzwords in the tech community. Let’s cut through the hype and, actually, figure out what Data Scientists and ML Engineers do, where their roles overlap and where they differ. Please note that this is an opinionated piece, and thoughts and ideas expressed in the article are the author’s only.
https://bit.ly/3oj8UJC
Data Science and Machine Learning have become buzzwords in the tech community. Let’s cut through the hype and, actually, figure out what Data Scientists and ML Engineers do, where their roles overlap and where they differ. Please note that this is an opinionated piece, and thoughts and ideas expressed in the article are the author’s only.
https://bit.ly/3oj8UJC
Meet skweak: A Python Toolkit For Applying Weak Supervision To NLP Tasks
Skweak is a Python toolkit developed for applying weak supervision to various NLP tasks. In this article, you will learn how to use skweak for such NLP tasks as labeling and text classification. The article is illustrated with a practical implementation for reference.
https://bit.ly/33LV9tA
Skweak is a Python toolkit developed for applying weak supervision to various NLP tasks. In this article, you will learn how to use skweak for such NLP tasks as labeling and text classification. The article is illustrated with a practical implementation for reference.
https://bit.ly/33LV9tA
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