💡PyTorch: Transfer Learning and Image Classification
In this tutorial, Adrian Rosebrock will explain how you can perform transfer learning for image classification using the PyTorch deep learning library. Check out part 1 of the tutorial.
https://bit.ly/3BSfLz1
In this tutorial, Adrian Rosebrock will explain how you can perform transfer learning for image classification using the PyTorch deep learning library. Check out part 1 of the tutorial.
https://bit.ly/3BSfLz1
PyImageSearch
PyTorch: Transfer Learning and Image Classification - PyImageSearch
In this tutorial, you will learn to perform transfer learning and image classification using the PyTorch deep learning library.
⚡️Hello everybody! Today's the day we're going to talk about Dion Hinchcliffe. He's an internationally recognized business strategist, enterprise architect, transformation consultant, futurist, analyst, and in-demand keynote speaker.
Dion's currently a VP and Principal Analyst at Constellation Research where he heads up research and global client advisory into CIO issues, the Future of Work, and emerging enterprise technology. Dion's an industry expert in such areas as enterprise IT, digital transformation, digital workplace, social collaboration, customer experience, API strategy, Enterprise 2.0, Web 2.0, social business, SOA, cloud computing, agile/DevOps, digital business, and IT strategy. His thought leadership can be found on ZDNet, Constellation Research, InformationWeek, ebizQ, On Digital Strategy, and the Enterprise Irregulars. He's also co-authored Web 2.0 Architectures for O’Reilly and the bestselling Social Business by Design (John Wiley & Sons).
https://bit.ly/3H0iFFH
Dion's currently a VP and Principal Analyst at Constellation Research where he heads up research and global client advisory into CIO issues, the Future of Work, and emerging enterprise technology. Dion's an industry expert in such areas as enterprise IT, digital transformation, digital workplace, social collaboration, customer experience, API strategy, Enterprise 2.0, Web 2.0, social business, SOA, cloud computing, agile/DevOps, digital business, and IT strategy. His thought leadership can be found on ZDNet, Constellation Research, InformationWeek, ebizQ, On Digital Strategy, and the Enterprise Irregulars. He's also co-authored Web 2.0 Architectures for O’Reilly and the bestselling Social Business by Design (John Wiley & Sons).
https://bit.ly/3H0iFFH
📚Wav2CLIP: Learning Robust Audio Representations From CLIP
Wav2CLIP is a robust audio representation learning method by distilling from CLIP. It can outperform publicly available pre-trained audio representation algorithms.
https://bit.ly/3kjbP4l
Wav2CLIP is a robust audio representation learning method by distilling from CLIP. It can outperform publicly available pre-trained audio representation algorithms.
https://bit.ly/3kjbP4l
📌Neural Radiance Field (NeRF) Papers at ICCV 2021
In anticipation of ICCV (Intl. Conf. on Computer Vision), the author rounded up all papers that use Neural Radiance Fields (NeRFs) that will be represented in the main ICCV2021 conference.
https://bit.ly/3wr3Rep
In anticipation of ICCV (Intl. Conf. on Computer Vision), the author rounded up all papers that use Neural Radiance Fields (NeRFs) that will be represented in the main ICCV2021 conference.
https://bit.ly/3wr3Rep
Frank Dellaert
NeRF at ICCV 2021
In anticipation of ICCV (Intl. Conf. on Computer Vision) this week, I rounded up all papers that use Neural Radiance Fields (NeRFs) that will be represented in the main #ICCV2021 conference.Many of the papers I discussed in my original blog-post on NerF made…
📚Natural Language Processing (NLP) course
During this course, the students can gain a comprehensive understanding of NLP, from the principles and theories of NLP to various NLP technologies. 12 lectures in total.
https://bit.ly/3mZofA4
During this course, the students can gain a comprehensive understanding of NLP, from the principles and theories of NLP to various NLP technologies. 12 lectures in total.
https://bit.ly/3mZofA4
Open Data Science (ODS.ai)
Natural Language Processing course — Open Data Science
💡A Gentle Introduction to Vector Space Models
In this tutorial, you'll learn about vector space, the properties of cosine similarity and how it can help you compare two vectors, and how cosine similarity and L2 distance are different.
https://bit.ly/3qsomX5
In this tutorial, you'll learn about vector space, the properties of cosine similarity and how it can help you compare two vectors, and how cosine similarity and L2 distance are different.
https://bit.ly/3qsomX5
MachineLearningMastery.com
A Gentle Introduction to Vector Space Models - MachineLearningMastery.com
Vector space models are to consider the relationship between data that are represented by vectors. It is popular in information retrieval systems but also useful for other purposes. Generally, this allows us to compare the similarity of two vectors from a…
⚡️Hello friends, we hope that your day is going great! Data Phoenix team wants to remind you 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/31HnjrQ
https://bit.ly/31HnjrQ
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.
📚Non-deep Networks
In this paper, Ankit Goyal et al. theorize and show that it is possible to build high-performing "non-deep" neural networks by using parallel subnetworks instead of stacking one layer after another.
https://bit.ly/3CdvGbg
In this paper, Ankit Goyal et al. theorize and show that it is possible to build high-performing "non-deep" neural networks by using parallel subnetworks instead of stacking one layer after another.
https://bit.ly/3CdvGbg
⚡️Hi friends!
Data Phoenix team is ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3ngpnQd
Data Phoenix team is ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3ngpnQd
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.
⚡️Hello friends! We hope that your weekend is going great!
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) Product Manager, Machine Learning - Grammarly, Kyiv, Remote
https://bit.ly/3osDO2G
2) Director of Data Operations - GitHub, Remote - US / Canada
https://bit.ly/3qxohBq
3) Machine Learning Engineer - Lyft, Kyiv
https://bit.ly/3HlSPfi
4) Data Scientist - Snap, Kyiv, Odesa, Remote
https://bit.ly/3wF8ulb
5) Research Scientist in HPC and AI Performance - Lawrence Berkeley National Lab, Bay Area, California, United States
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info
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) Product Manager, Machine Learning - Grammarly, Kyiv, Remote
https://bit.ly/3osDO2G
2) Director of Data Operations - GitHub, Remote - US / Canada
https://bit.ly/3qxohBq
3) Machine Learning Engineer - Lyft, Kyiv
https://bit.ly/3HlSPfi
4) Data Scientist - Snap, Kyiv, Odesa, Remote
https://bit.ly/3wF8ulb
5) Research Scientist in HPC and AI Performance - Lawrence Berkeley National Lab, Bay Area, California, United States
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info
📌 MLOps and DevOps: Why Data Makes It Different
In this article, the O'Reilly team digs into the fundamentals of machine learning as an engineering discipline to answer key questions about MLOps, DevOps, and their evolution through data.
https://bit.ly/31ZNA51
In this article, the O'Reilly team digs into the fundamentals of machine learning as an engineering discipline to answer key questions about MLOps, DevOps, and their evolution through data.
https://bit.ly/31ZNA51
O’Reilly Media
MLOps and DevOps: Why Data Makes It Different
Machine Learning’s deployment stack is maturing
We hope your Sunday is going great and you are ready for the upcoming week!
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3ccpLbO
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3ccpLbO
📚SSAST: Self-Supervised Audio Spectrogram Transformer
In this paper, the authors aim to alleviate the data requirement issues with the AST by leveraging self-supervised learning using unlabeled data for audio and speech classification.
https://bit.ly/3qCwlky
In this paper, the authors aim to alleviate the data requirement issues with the AST by leveraging self-supervised learning using unlabeled data for audio and speech classification.
https://bit.ly/3qCwlky
⚡️Happy Monday, friends! Today we want to feature Hilary Mason, the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in residence at Accel.
Previously, she was Chief Scientist at Bitly, co-founded of HackNY, and co-hosted DataGotham. She is a member of NYCResistor. As a Data Scientist in residence at Accel, she provides consulting services to companies large and small about their data strategy.
Hilary spent four years as Chief Scientist at Bitly, where she led the team that studied attention on the internet in real time, doing a mix of research, exploration, and engineering. Also, she co-founded HackNY, a non-profit that helps talented engineering students find their way into the startup community of creative technologists in New York City.
Hilton was a member of Mayor Bloomberg’s Technology and Innovation Advisory Council, which was a great way for her to learn how government and industry can work together.
Did you know about Hilary before? Let us know what you think!😉
https://bit.ly/3ChZWSz
Previously, she was Chief Scientist at Bitly, co-founded of HackNY, and co-hosted DataGotham. She is a member of NYCResistor. As a Data Scientist in residence at Accel, she provides consulting services to companies large and small about their data strategy.
Hilary spent four years as Chief Scientist at Bitly, where she led the team that studied attention on the internet in real time, doing a mix of research, exploration, and engineering. Also, she co-founded HackNY, a non-profit that helps talented engineering students find their way into the startup community of creative technologists in New York City.
Hilton was a member of Mayor Bloomberg’s Technology and Innovation Advisory Council, which was a great way for her to learn how government and industry can work together.
Did you know about Hilary before? Let us know what you think!😉
https://bit.ly/3ChZWSz
📚The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Learn about the solution!
https://bit.ly/3qF6usi
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Learn about the solution!
https://bit.ly/3qF6usi
💡Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge
Learn how to create the cloud to edge solution with Amazon SageMaker to detect defective parts from a real-time stream of images sent to an edge device. A demo included.
https://go.aws/30ny7ea
Learn how to create the cloud to edge solution with Amazon SageMaker to detect defective parts from a real-time stream of images sent to an edge device. A demo included.
https://go.aws/30ny7ea
Amazon
Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge | Amazon Web Services
Defect detection in manufacturing can benefit from machine learning (ML) and computer vision (CV) to reduce operational costs, improve time to market, and increase productivity, quality, and safety. According to McKinsey, the “benefits of defect detection…
📚Alias-Free Generative Adversarial Networks
The researchers trace the root cause to careless signal processing that causes aliasing in the generator network and derive architectural changes that guarantee better results.
https://bit.ly/3qHf6ys
The researchers trace the root cause to careless signal processing that causes aliasing in the generator network and derive architectural changes that guarantee better results.
https://bit.ly/3qHf6ys
📌Training a DCGAN in PyTotch
In this tutorial, you'll learn how to train our first DCGAN Model using PyTorch to generate images. Check out Part 2 and Part 3 of the series
on Advanced PyTorch Techniques.
https://bit.ly/3qOyRUU
In this tutorial, you'll learn how to train our first DCGAN Model using PyTorch to generate images. Check out Part 2 and Part 3 of the series
on Advanced PyTorch Techniques.
https://bit.ly/3qOyRUU
PyImageSearch
Training a DCGAN in PyTorch - PyImageSearch
Learn to train a DCGAN using PyTorch and Python. This tutorial is perfect for coders comfortable with PyTorch and Generative Adversarial Networks.
⚡️Hello everyone, we hope that your day is going great! Data Phoenix team wants to remind you 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/3nt5QMd
https://bit.ly/3nt5QMd
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.
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VIEW IN TELEGRAM
Join Dr. Dean and Dr. Bazhirov on November 19th to learn more about Interpretable Machine Learning for materials research and development.
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The exponential growth of interest in Machine Learning has led to a wealth of data in recent years, which has corresponded with an expansion in the number of techniques available. In this webinar, we benchmark a few recent techniques, including SISSO (Symbolic Regression), TPOT (AutoML), Roost (deep learning algorithms), and XGBoost (Gradient-boosting) to predict the properties of perovskites and 2dmaterials.
Register at https://bit.ly/3nuAp4h
==
The exponential growth of interest in Machine Learning has led to a wealth of data in recent years, which has corresponded with an expansion in the number of techniques available. In this webinar, we benchmark a few recent techniques, including SISSO (Symbolic Regression), TPOT (AutoML), Roost (deep learning algorithms), and XGBoost (Gradient-boosting) to predict the properties of perovskites and 2dmaterials.
Register at https://bit.ly/3nuAp4h
💡Serving ML Models in Production: Common Patterns
This article explores Ray Serve, a service combining pipelines, ensemble, business logic, and online learning for machine learning. Learn how to use the service for serving ML models in production.
https://bit.ly/3criUeG
This article explores Ray Serve, a service combining pipelines, ensemble, business logic, and online learning for machine learning. Learn how to use the service for serving ML models in production.
https://bit.ly/3criUeG
Anyscale
Serving ML Models in Production: Common Patterns | Anyscale
In this post, we explore the 4 common patterns of ML in production and how to implement these patterns using Ray Serve.