💡Robust High-Resolution Video Matting with Temporal Guidance
In this paper, you'll find a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance and is much lighter than previous approaches.
https://bit.ly/2Yh5vSX
In this paper, you'll find a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance and is much lighter than previous approaches.
https://bit.ly/2Yh5vSX
📌The FP Growth algorithm
In this exploratory post, you'll be guided through a series of steps to apply the FP Growth algorithm in Python, in order to do frequent itemset mining for basket analysis.
https://bit.ly/3Af74xU
In this exploratory post, you'll be guided through a series of steps to apply the FP Growth algorithm in Python, in order to do frequent itemset mining for basket analysis.
https://bit.ly/3Af74xU
Medium
The FP Growth algorithm
Using the FP Growth algorithm in Python to do frequent itemset mining for basket analysis
Hi friends! Don't forget about our weekly newsletter which is coming tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3FpDDgc
https://bit.ly/3FpDDgc
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
💡Instance-Conditioned GAN
GANs can generate photo-realistic images. In this paper, the authors use kernel density estimation techniques to introduce a non-parametric approach to modeling distributions of complex datasets.
https://bit.ly/3Ah8yrh
GANs can generate photo-realistic images. In this paper, the authors use kernel density estimation techniques to introduce a non-parametric approach to modeling distributions of complex datasets.
https://bit.ly/3Ah8yrh
📌Apache Spark Monitoring: How To Use Spark API & Open-Source Libraries To Get Better Data Observability Of Your Application
In this guide, you’ll learn how to ensure data observability in Spark using Spark’s internal systems like Listener APIs and Query Execution Listeners, and libraries to track data quality metrics.
https://bit.ly/3oK12D9
In this guide, you’ll learn how to ensure data observability in Spark using Spark’s internal systems like Listener APIs and Query Execution Listeners, and libraries to track data quality metrics.
https://bit.ly/3oK12D9
Databand
Apache Spark Monitoring using Listener APIs and Data quality Libraries
See how to use Listener APIs and data quality libraries to get different levels of data observability for Apache Spark.
With a small delay, we are ready to present our weekly issue of the digest! Later but with high quality😉
And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3mA1jpM
P.S. Data Phoenix team appreciates you and thanks to you for being here every week!
And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3mA1jpM
P.S. Data Phoenix team appreciates you and thanks to you for being here every week!
Data Phoenix
Data Phoenix Digest - ISSUE 26
New insights into ML, AI, and Data (MAD) landscape, OECD report on investment in AI, China's new take on regulating AI, the FP Growth algorithm, parallelizing Python code, intro to GANs, instance-conditioned GAN, SwinlR, RSDet++, tools, jobs, and more ...
💡Fake It Till You Make It
In this research, the AI/ML team at Microsoft demonstrates that it is possible to perform face-related computer vision in the wild using synthetic data alone.
https://bit.ly/3iMferx
In this research, the AI/ML team at Microsoft demonstrates that it is possible to perform face-related computer vision in the wild using synthetic data alone.
https://bit.ly/3iMferx
⚡️Hello guys! How is your weekend going?⚡️
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Computational Materials Scientist AI / ML - Exabyte.io, San Francisco, Remote
https://bit.ly/3lpZyvR
2) Senior Python Developer - VITECH, Lviv, Ivano-Frankivsk, Remote
https://bit.ly/3iLuCEA
3) Postdoctoral Scholar in HPC and AI Performance - Lawrence Berkeley National Lab, Bay Area, California
https://bit.ly/3aqbmrP
4) Senior Data Scientist - Reddit, New York
https://bit.ly/3uUPs9B
5) Data Scientist, Algorithms - Lyft, San Francisco
https://bit.ly/3amD5tn
For the other 5 positions click 👉🏻 https://bit.ly/30aI3HT
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Computational Materials Scientist AI / ML - Exabyte.io, San Francisco, Remote
https://bit.ly/3lpZyvR
2) Senior Python Developer - VITECH, Lviv, Ivano-Frankivsk, Remote
https://bit.ly/3iLuCEA
3) Postdoctoral Scholar in HPC and AI Performance - Lawrence Berkeley National Lab, Bay Area, California
https://bit.ly/3aqbmrP
4) Senior Data Scientist - Reddit, New York
https://bit.ly/3uUPs9B
5) Data Scientist, Algorithms - Lyft, San Francisco
https://bit.ly/3amD5tn
For the other 5 positions click 👉🏻 https://bit.ly/30aI3HT
Good morning everyone! Hope you have a great weekend! Here's your dose of positivity as always for this Sunday!🤗
https://bit.ly/2YJtQB8
https://bit.ly/2YJtQB8
Hello everyone! Hope you had a great start of the week! Data Phoenix would like to make your Monday even better and to talk about Bill Schmarzo, one of the top Digital Transformation influencers on Big Data and Data Science. If you don’t follow him yet, kindly check this out!
Bill Schmarzo worked in data warehousing, advanced analytics, and BI niches for over 30 years. As the current CTO, Analytics and IoT for Hitachi Vantara, “The Dean of Big Data” guides the company’s technology strategy and drives “co-creation” efforts with select customers, to leverage IoT and analytics capacity powering digital transformation.
Bill is also famous as a writer. He has published three books: "The Art of Thinking Like a Data Scientist," "Big Data," and "Big Data MBA". Books aside, he is the author of over 300 articles and blog posts on how organizations can become more effective at leveraging data and analytics to power their business models.
https://bit.ly/3FsvwzA
https://bit.ly/3iP2b8U
Bill Schmarzo worked in data warehousing, advanced analytics, and BI niches for over 30 years. As the current CTO, Analytics and IoT for Hitachi Vantara, “The Dean of Big Data” guides the company’s technology strategy and drives “co-creation” efforts with select customers, to leverage IoT and analytics capacity powering digital transformation.
Bill is also famous as a writer. He has published three books: "The Art of Thinking Like a Data Scientist," "Big Data," and "Big Data MBA". Books aside, he is the author of over 300 articles and blog posts on how organizations can become more effective at leveraging data and analytics to power their business models.
https://bit.ly/3FsvwzA
https://bit.ly/3iP2b8U
📌Reinforcement Learning Lecture Series 2021 [DeepMind]
The course offers students 13 lectures on the fundamentals of reinforcement learning and planning in sequential decision problems, as well as more advanced topics and modern deep RL algorithms.
https://bit.ly/3BAEdpl
The course offers students 13 lectures on the fundamentals of reinforcement learning and planning in sequential decision problems, as well as more advanced topics and modern deep RL algorithms.
https://bit.ly/3BAEdpl
📚The-Art-of-Linear-Algebra
The-Art-of-Linear-Algebra is a useful resource that features intuitive visualizations of important concepts introduced in "Linear Algebra for Everyone" by Gilbert Strang. Available as a PDF file.
https://bit.ly/3BCr4w3
The-Art-of-Linear-Algebra is a useful resource that features intuitive visualizations of important concepts introduced in "Linear Algebra for Everyone" by Gilbert Strang. Available as a PDF file.
https://bit.ly/3BCr4w3
GitHub
GitHub - kenjihiranabe/The-Art-of-Linear-Algebra: Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone" - GitHub - kenjihiranabe/The-Art-of-Linear-Algebra: Graphic notes on Gilbert Strang's "Lin...
Hello everyone! Are you onboard and receive our weekly newsletter? Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3v7cHNG
https://bit.ly/3v7cHNG
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
🎥Graph Neural Network for Lagrangian Simulation
A lecture on Graph Neural Network for Lagrangian Simulation delivered at American Physical Society - Division of Fluid Dynamics Annual Meeting by Zijie Li of Mechanical and AI Lab.
https://bit.ly/30r1n3K
A lecture on Graph Neural Network for Lagrangian Simulation delivered at American Physical Society - Division of Fluid Dynamics Annual Meeting by Zijie Li of Mechanical and AI Lab.
https://bit.ly/30r1n3K
YouTube
Graph Neural Network for Lagrangian Simulation - Zijie Li
MAIL Website: http://baratilab.com
Presented at American Physical Society - Division of Fluid Dynamics Annual Meeting (APS-DFD 2020)
Fluid Simulations with Graph Neural Networks:
Water Fall: https://youtu.be/zZ1NuFZGgVE
Dam: https://youtu.be/NGpBanlouLI…
Presented at American Physical Society - Division of Fluid Dynamics Annual Meeting (APS-DFD 2020)
Fluid Simulations with Graph Neural Networks:
Water Fall: https://youtu.be/zZ1NuFZGgVE
Dam: https://youtu.be/NGpBanlouLI…
📌Geometric Deep Learning [Course]
GDL is a free course that closely follows the contents of GDL proto-book by Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. All materials and artefacts are publicly available.
https://bit.ly/3FRpDvW
GDL is a free course that closely follows the contents of GDL proto-book by Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. All materials and artefacts are publicly available.
https://bit.ly/3FRpDvW
Geometricdeeplearning
GDL Course
Grids, Groups, Graphs, Geodesics, and Gauges
Hello friends! Data Phoenix team would like to make an announcement. Now, we'll release our weekly digest on Friday. We believe that it'll give you more time to check it out, and we'll be able to provide even more high-quality content to you. Hope you aren't against the change and will love what we have in store for you. So, see you a bit later today!
https://bit.ly/3vsFQmR
https://bit.ly/3vsFQmR
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
Hey friends! Data Phoenix is here and we want to tell you that the latest issue of the digest is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3DLEKW8
https://bit.ly/3DLEKW8
Data Phoenix
Data Phoenix Digest - ISSUE 27
Data Science automation is here, EU gets closer to banning facial recognition, serving ML Models in production, localizing objects with self-supervised transformers and no labels, courses, videos, tools, jobs, and more ...
⚡️Hello friends! How is your weekend going?
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Computational Materials Scientist AI / ML - Exabyte.io, San Francisco, Remote
https://bit.ly/3p5mpiE
2) Data Scientist Demand - TripAdvisor, Paris, France
https://bit.ly/2YOnikx
3) Marketing Data Scientist - Intercom, Dublin, Ireland
https://bit.ly/3mYJy3J
4) Data Scientist II - AWS, Seattle, Washington
https://bit.ly/3mYJyAL
5) Sr Data Scientist, Machine Learning - Coursera, Canada (Remote)
https://bit.ly/3lLp4fa
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Computational Materials Scientist AI / ML - Exabyte.io, San Francisco, Remote
https://bit.ly/3p5mpiE
2) Data Scientist Demand - TripAdvisor, Paris, France
https://bit.ly/2YOnikx
3) Marketing Data Scientist - Intercom, Dublin, Ireland
https://bit.ly/3mYJy3J
4) Data Scientist II - AWS, Seattle, Washington
https://bit.ly/3mYJyAL
5) Sr Data Scientist, Machine Learning - Coursera, Canada (Remote)
https://bit.ly/3lLp4fa
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
Good morning! Hope you have a great weekend! Here's your dose of positivity as always for this Sunday!🤗
https://bit.ly/3pazd7i
https://bit.ly/3pazd7i
📌The Human Regression Ensemble
In this article, Justin Domke plays with the idea that humans can do predictions as well as algorithms for simple tasks. Let's check out what's come out of it!
https://bit.ly/3DLIjvq
In this article, Justin Domke plays with the idea that humans can do predictions as well as algorithms for simple tasks. Let's check out what's come out of it!
https://bit.ly/3DLIjvq
Justin Domke
The human regression ensemble
I sometimes worry that people credit machine learning with magical powers. Friends from other fields often show me little datasets. Maybe they measured the concentration of a protein in some cell l…
⚡️Hey guys! We'd like to start this Monday with a story of Carla Gentry - a Data Scientist and founder of Analytical-Solution.
She is a great example of a strong woman (and we do love that!). Being a single mother of two sons was a challenge, but despite all the circumstances, she was eager to learn and grow. She worked in the Developmental Math Lab her entire tenure with UTC, assisting students with all levels of mathematics. After graduating from UTC with a double major in Applied Mathematics and Economics in 1998, she moved to the Chicago area to start her career in analytics.
During the last 19+ years, she has worked for such companies as Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil, and Firestone. She says that being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance.
https://bit.ly/3AQbKL4
She is a great example of a strong woman (and we do love that!). Being a single mother of two sons was a challenge, but despite all the circumstances, she was eager to learn and grow. She worked in the Developmental Math Lab her entire tenure with UTC, assisting students with all levels of mathematics. After graduating from UTC with a double major in Applied Mathematics and Economics in 1998, she moved to the Chicago area to start her career in analytics.
During the last 19+ years, she has worked for such companies as Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil, and Firestone. She says that being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance.
https://bit.ly/3AQbKL4