Did you know that the #
Python style guide (PEP8) offers guidance on how you ought to import multiple modules?
Python style guide (PEP8) offers guidance on how you ought to import multiple modules?
Every week, the top AI labs globally — Google, Facebook, Microsoft, Apple, etc. — release tons of new ML research work, tools, datasets, models, libraries and frameworks.
Interestingly, they all seem to have picked a particular school of thought in deep learning. With time, this pattern is becoming more and more clear.
DeepMind remains synonymous with reinforcement learning. From AlphaGo to MuZero and the recent AlphaFold, the company has been championing breakthroughs in reinforcement learning.
Google is advancing AutoML in highly diverse areas like time-series analysis and computer vision.
Apple, in the last few years, has ventured into federated learning.
Microsoft Research is pioneering machine teaching research and technology in computer vision and speech analysis.
Amazon has become one of the leading research hubs for transfer learning methods due to its exceptional work in the Alexa digital assistant.
IBM is pushing its research boundaries in quantum machine learning.
Interestingly, they all seem to have picked a particular school of thought in deep learning. With time, this pattern is becoming more and more clear.
DeepMind remains synonymous with reinforcement learning. From AlphaGo to MuZero and the recent AlphaFold, the company has been championing breakthroughs in reinforcement learning.
Google is advancing AutoML in highly diverse areas like time-series analysis and computer vision.
Apple, in the last few years, has ventured into federated learning.
Microsoft Research is pioneering machine teaching research and technology in computer vision and speech analysis.
Amazon has become one of the leading research hubs for transfer learning methods due to its exceptional work in the Alexa digital assistant.
IBM is pushing its research boundaries in quantum machine learning.
*** Big Breaking ***
Python 3.10 is released today! pythonistas. 🐍 🎊
************************
Python 3.10 is released today! pythonistas. 🐍 🎊
************************
Bringing VS Code to the browser
Fast forward to today. Now when you go to https://vscode.dev, you'll be presented with a lightweight version of VS Code running fully in the browser. Open a folder on your local machine and start coding.
No install required.
Fast forward to today. Now when you go to https://vscode.dev, you'll be presented with a lightweight version of VS Code running fully in the browser. Open a folder on your local machine and start coding.
No install required.
vscode.dev
Visual Studio Code for the Web
Build with Visual Studio Code, anywhere, anytime, entirely in your browser.
Top 8 Blogs for Deep learning you should learn in 2022...✔️✔️✔️
Why you should follow this blog?
👉 Blogs assist you to stay up to date with newly published technology, research, methods, and learning experiences people share with them.
Top 8 Blogs you must read to learn deep learning:
-------------------------------------------------------
1. Towards Data Science: https://lnkd.in/gAB7QSnp
2. Medium.com: https://lnkd.in/gbJaXneh
3. Analytics Vidhya: https://lnkd.in/gk2jevgB
4. KDnuggets: https://lnkd.in/gZUUReq2
5. neptune.ai - https://lnkd.in/gsNksnAD
6. Machine Learning Mastery- https://lnkd.in/gPZft6x3
7. PyImageSearch - https://lnkd.in/gBJUKMby
8. Kaggle - https://lnkd.in/g_3AvFz
9. 100 Blog lists for Data Scientist by Insane: https://lnkd.in/gpsJaveu
Why you should follow this blog?
👉 Blogs assist you to stay up to date with newly published technology, research, methods, and learning experiences people share with them.
Top 8 Blogs you must read to learn deep learning:
-------------------------------------------------------
1. Towards Data Science: https://lnkd.in/gAB7QSnp
2. Medium.com: https://lnkd.in/gbJaXneh
3. Analytics Vidhya: https://lnkd.in/gk2jevgB
4. KDnuggets: https://lnkd.in/gZUUReq2
5. neptune.ai - https://lnkd.in/gsNksnAD
6. Machine Learning Mastery- https://lnkd.in/gPZft6x3
7. PyImageSearch - https://lnkd.in/gBJUKMby
8. Kaggle - https://lnkd.in/g_3AvFz
9. 100 Blog lists for Data Scientist by Insane: https://lnkd.in/gpsJaveu
Deep Learning – Towards Data Science
Read writing about Deep Learning in Towards Data Science. Your home for data science. A Medium publication sharing concepts, ideas and codes.
Ten HTTP status codes you see most often. 👇🏻
• 200 Ok
Successful request
• 201 Created
New resource was created
• 204 No Content
Successful request but no content
• 304 Not Modified
Use cached version
• 400 Bad Request
Malformed request syntax or message framing
• 401 Unauthorized
Client must authenticate itself
• 403 Forbidden
No access to the content
• 404 Not Found
URL is not recognized
• 429 Too Many Requests
Too many requests in a given timeframe
• 500 Internal Server Error
An unexpected situation that server can't handle
• 200 Ok
Successful request
• 201 Created
New resource was created
• 204 No Content
Successful request but no content
• 304 Not Modified
Use cached version
• 400 Bad Request
Malformed request syntax or message framing
• 401 Unauthorized
Client must authenticate itself
• 403 Forbidden
No access to the content
• 404 Not Found
URL is not recognized
• 429 Too Many Requests
Too many requests in a given timeframe
• 500 Internal Server Error
An unexpected situation that server can't handle
While looking for an NLP domain to research, I found this amazing resource: https://nlpprogress.com/
It lists out the progress that has been made so far in a variety of different domains so researchers can have a better idea on their areas of improvement before selecting one and diving into a proper literature review -- or just to test out models for their application.
Each section includes the top papers of the domain, the scores achieved in their implementation, and even the code. It's a lot like paperswithcode.com but specifically for NLP.
It lists out the progress that has been made so far in a variety of different domains so researchers can have a better idea on their areas of improvement before selecting one and diving into a proper literature review -- or just to test out models for their application.
Each section includes the top papers of the domain, the scores achieved in their implementation, and even the code. It's a lot like paperswithcode.com but specifically for NLP.
NLP-progress
Tracking Progress in Natural Language Processing
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Debugging NLP models is hard. AdaTest, a new open-source tool, combines the strengths of automated and user-driven methods, leveraging the generative capabilities of large language models and human expertise in an iterative testing and debugging loo
https://github.com/microsoft/adatest
https://github.com/microsoft/adatest
GitHub
GitHub - microsoft/adatest: Find and fix bugs in natural language machine learning models using adaptive testing.
Find and fix bugs in natural language machine learning models using adaptive testing. - microsoft/adatest