e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks (VL-NLE)
https://arxiv.org/abs/2105.03761
#پردازش_زبان_طبیعی #توضیح_پردازش_زبان_طبیعی #مقاله
❇️ @AI_Python
https://arxiv.org/abs/2105.03761
#پردازش_زبان_طبیعی #توضیح_پردازش_زبان_طبیعی #مقاله
❇️ @AI_Python
Machine learning offers high-definition glimpse of how genomes organize in #singlecells
https://phys.org/news/2021-10-machine-high-definition-glimpse-genomes-cells.html
#مقاله #یادگیری_ماشین #مقاله #ژنتیک #علوم_پزشکی
❇️ @AI_Python
https://phys.org/news/2021-10-machine-high-definition-glimpse-genomes-cells.html
#مقاله #یادگیری_ماشین #مقاله #ژنتیک #علوم_پزشکی
❇️ @AI_Python
AI as an API - Part 1
Learn how to turn a ML Trained Model into an REST API using Keras, FastAPI & NoSQL.
https://www.youtube.com/watch?v=56qQNcHJxyQ
#منابع #فیلم
❇️ @AI_Python
Learn how to turn a ML Trained Model into an REST API using Keras, FastAPI & NoSQL.
https://www.youtube.com/watch?v=56qQNcHJxyQ
#منابع #فیلم
❇️ @AI_Python
YouTube
AI as an API - Part 1 - Train an ML Model and turn it into an Rest API using Keras, FastAPI & NoSQL
AI as an API - Part 1 - Turn a ML Trained Model into an Rest API using Keras, FastAPI & NoSQL
PART 2: https://youtu.be/nTdMjFcK3SM
Learn to create & deploy a deep learning algorithm into a production REST API using Python, Keras, FastAPI, & NoSQL.
⦿ Build…
PART 2: https://youtu.be/nTdMjFcK3SM
Learn to create & deploy a deep learning algorithm into a production REST API using Python, Keras, FastAPI, & NoSQL.
⦿ Build…
👍2
12 عمل که کیفیت داده ها را بالا ببریم
https://buff.ly/3BuBEoX
#منابع #آموزش_کلاسی #آموزش
❇️ @AI_Python
https://buff.ly/3BuBEoX
#منابع #آموزش_کلاسی #آموزش
❇️ @AI_Python
Forwarded from Django Expert (Mohammad Babazadeh)
💵 درآمد برنامه نویسی چقدر هست؟
در این ویدئو راجع به معامله هایی که توی اتاق هایی با درهای بسته انجام میشه، صحبت میکنیم 😎💰
🖥 مشاهده در یوتوب:
👉 Link: https://youtu.be/uwJpBPqyBzI
#برنامه_نویسی #درآمد #درآمد_برنامه_نویسی
〰️〰️〰️〰️〰️
© @DjangoEx | @AI_Python
در این ویدئو راجع به معامله هایی که توی اتاق هایی با درهای بسته انجام میشه، صحبت میکنیم 😎💰
🖥 مشاهده در یوتوب:
👉 Link: https://youtu.be/uwJpBPqyBzI
#برنامه_نویسی #درآمد #درآمد_برنامه_نویسی
〰️〰️〰️〰️〰️
© @DjangoEx | @AI_Python
Best Data Science Blogs That Every Data Analyst Should Follow
#DataScience #MachineLearning #ML #AI #DataScienceCommunity #DataScientist
https://reconshell.com/best-data-science-blogs-that-every-data-analyst-should-follow/
❇️ @AI_Python
#DataScience #MachineLearning #ML #AI #DataScienceCommunity #DataScientist
https://reconshell.com/best-data-science-blogs-that-every-data-analyst-should-follow/
❇️ @AI_Python
کلیه مقالات کنفرانس ICCV2021 در دسترس قرار گرفت
https://openaccess.thecvf.com/ICCV2021
#هوش_مصنوعی #الگوریتمها #آموزش #کنفرانس
❇️ @AI_Python
https://openaccess.thecvf.com/ICCV2021
#هوش_مصنوعی #الگوریتمها #آموزش #کنفرانس
❇️ @AI_Python
How do I get professors to answer my emails?
You have sent SO MANY emails to professors for scheduling your prelim, looking for research opportunities, or asking questions about courses etc. But they just don't reply at all! Angry face
How do I get things done? A few tips below .
Group scheduling*
Don't send out Doodle/When2meet with 30+ entries! Look up their (course) schedules and propose only a few (3-5) time slots.
If they need to spend 15 mins filling out your scheduling request, they will simply ignore your email.
*Give them the control for planning their day*
Scheduling a meeting:
Don't: When will you be available?
Do: I am available ... Will you be available in one of the time slots?
When you ask for availability, you are effectively asking for commitment for ALL of the time slots.
*Calendar invite*
Once you scheduled a meeting. Please do send a calendar invite with all the required information (e.g., zoom link).
Whatever that's not on their calendar does not exist.
*Go 90% and let them finish the 10%*
• Need a form? Pre-fill it as much as you can (you know their name and email).
• Need a cover letter? Write a draft first.
• Need a ref. letter? Send an updated CV and SOP.
• Need to ask about a course? Read the syllabus first.
*cc all parties involved*
Asking for some code/data/clarification? cc'ing your PI and their PI definitely helps you get a prompt reply!
*Don't cc all parties involved*
Don't send requests to MULTIPLE people in the same thread. Everyone will assume others will do it and therefore no one will do it. Send INDIVIDUAL emails instead.
*One email one topic*
Help your professors respond your email quickly. Make your emails simple, clear, and actionable.
*Formatting*
You are not writing in plain texts.
Formatting your email so that it's easy to read, e.g.,
• bullet points for parallel concepts
• bold font for highlighting
• bold phrase for organizing your email
• italic for sentence stress.
*Timeline*
Provide specific action and specific date that the task needed to be completed. This helps them plan their schedule to make time for your requests.
#منابع #اپلای
❇️ @AI_Python
You have sent SO MANY emails to professors for scheduling your prelim, looking for research opportunities, or asking questions about courses etc. But they just don't reply at all! Angry face
How do I get things done? A few tips below .
Group scheduling*
Don't send out Doodle/When2meet with 30+ entries! Look up their (course) schedules and propose only a few (3-5) time slots.
If they need to spend 15 mins filling out your scheduling request, they will simply ignore your email.
*Give them the control for planning their day*
Scheduling a meeting:
Don't: When will you be available?
Do: I am available ... Will you be available in one of the time slots?
When you ask for availability, you are effectively asking for commitment for ALL of the time slots.
*Calendar invite*
Once you scheduled a meeting. Please do send a calendar invite with all the required information (e.g., zoom link).
Whatever that's not on their calendar does not exist.
*Go 90% and let them finish the 10%*
• Need a form? Pre-fill it as much as you can (you know their name and email).
• Need a cover letter? Write a draft first.
• Need a ref. letter? Send an updated CV and SOP.
• Need to ask about a course? Read the syllabus first.
*cc all parties involved*
Asking for some code/data/clarification? cc'ing your PI and their PI definitely helps you get a prompt reply!
*Don't cc all parties involved*
Don't send requests to MULTIPLE people in the same thread. Everyone will assume others will do it and therefore no one will do it. Send INDIVIDUAL emails instead.
*One email one topic*
Help your professors respond your email quickly. Make your emails simple, clear, and actionable.
*Formatting*
You are not writing in plain texts.
Formatting your email so that it's easy to read, e.g.,
• bullet points for parallel concepts
• bold font for highlighting
• bold phrase for organizing your email
• italic for sentence stress.
*Timeline*
Provide specific action and specific date that the task needed to be completed. This helps them plan their schedule to make time for your requests.
#منابع #اپلای
❇️ @AI_Python
Deep learning helps predict traffic crashes before they happen:
https://bit.ly/3FOULMX
#یادگیری_عمیق #آموزش_کلاسی #آموزش #منابع
❇️ @AI_Python
https://bit.ly/3FOULMX
#یادگیری_عمیق #آموزش_کلاسی #آموزش #منابع
❇️ @AI_Python
Free 10 hour #machinelearning course:
http://bit.ly/3uZiPrc
#فیلم #منابع #یادگیری_ماشین #کلاس_آموزشی
❇️ @AI_Python
http://bit.ly/3uZiPrc
#فیلم #منابع #یادگیری_ماشین #کلاس_آموزشی
❇️ @AI_Python
8-bit optimizers are mostly useful to finetune large models that did not fit into memory before. It is also easier to pretrain larger models and it has great synergy with sharded data parallelism. 8-bit Adam is already used across multiple teams in Facebook.
The CUDA-heavy bitsandbytes library was designed to be easy to use/install. It features 8-bit Adam/AdamW, SGD momentum, LARS, LAMB, and RMSProp, as well as high-performance quantization routines. It only requires a two-line code change to get you started!
Paper:
https://arxiv.org/abs/2110.02861
Library:
https://github.com/facebookresearch/bitsandbytes
Video:
https://www.youtube.com/watch?v=IxrlHAJtqKE
#مقاله #یادگیری_عمیق #هوش_مصنوعی #آموزش
❇️ @AI_Python
The CUDA-heavy bitsandbytes library was designed to be easy to use/install. It features 8-bit Adam/AdamW, SGD momentum, LARS, LAMB, and RMSProp, as well as high-performance quantization routines. It only requires a two-line code change to get you started!
Paper:
https://arxiv.org/abs/2110.02861
Library:
https://github.com/facebookresearch/bitsandbytes
Video:
https://www.youtube.com/watch?v=IxrlHAJtqKE
#مقاله #یادگیری_عمیق #هوش_مصنوعی #آموزش
❇️ @AI_Python
شرکت همکار ماندگار به منظور گسترش تیم فنی خود به دنبال تعدادی نیرو توسعه دهنده Front-end می باشد.
میزان حقوق پرداختی بسته به میزان مهارت شما و بر اساس توانمندی‌های شما می‌باشد.
مشهد(حضوری)
⚙️ مهارت ها
✔️ آشنایی کامل با JavaScript
✔️ آشنایی با React
✔️سابقه کار با Next.js
✔️آشنایی با Material UI
✔️ آشنایی Git
✔️تسلط بر مفاهیم Resposive web Design
hamekar.ir
ارسال رزومه:
@Hamekar_ir
Info@linkup.ir
https://quera.ir/j/15436
میزان حقوق پرداختی بسته به میزان مهارت شما و بر اساس توانمندی‌های شما می‌باشد.
مشهد(حضوری)
⚙️ مهارت ها
✔️ آشنایی کامل با JavaScript
✔️ آشنایی با React
✔️سابقه کار با Next.js
✔️آشنایی با Material UI
✔️ آشنایی Git
✔️تسلط بر مفاهیم Resposive web Design
hamekar.ir
ارسال رزومه:
@Hamekar_ir
Info@linkup.ir
https://quera.ir/j/15436
Learning from Rules Generalizing Labeled Exemplars
Leverage knowledge from annotators with examples+rules. Joint-network learns to classify & denoise rules
GitHub: https://github.com/awasthiabhijeet/Learning-From-Rules
Paper: https://bit.ly/3iXyceV
#python #NLP
#مقاله #پردازش_زبان_طبیعی #پایتون
❇️ @AI_Python
Leverage knowledge from annotators with examples+rules. Joint-network learns to classify & denoise rules
GitHub: https://github.com/awasthiabhijeet/Learning-From-Rules
Paper: https://bit.ly/3iXyceV
#python #NLP
#مقاله #پردازش_زبان_طبیعی #پایتون
❇️ @AI_Python
همه مطالب و آموزشهای دکتر رضوی به صورت رایگان در کانال یوتیوب ایشان در دسترس علاقمندان می باشد آموزشهای کلاسی دکتر رضوی همیشه به صورت رایگان منتشر میگردد و منتشر شده است اگر کسانی به اسم فروش دوره های دکتر رضوی پیامی منتشر کرد خلاف اصول اخلاقی و اکادمیکی هست که با اسم ایشان تجارت کنند و تمامی مطالب ایشان توسط یوتیوبشون به صورت رایگان برای همگان منتشر میشود یوتیوب ایشان را دنبال کنید.
https://www.youtube.com/c/SeyedNaserRazavi
#فیلم #دکتر_رضوی #آموزش_کلاسی #منابع #هوش_مصنوعی #یادگیری_عمیق #شبکه_عصبی
❇️ @AI_Python
https://www.youtube.com/c/SeyedNaserRazavi
#فیلم #دکتر_رضوی #آموزش_کلاسی #منابع #هوش_مصنوعی #یادگیری_عمیق #شبکه_عصبی
❇️ @AI_Python
Forwarded from DLeX: AI Python (Farzad 🦅)
Media is too big
VIEW IN TELEGRAM
آموزش های هوش مصنوعی دکتر اله یاری
RNN and LSTM Networks Tutorial Part 1
#منابع #فیلم #دانشگاه #الگوریتمها #کلاس_آموزشی #یادگیری_ماشین #هوش_مصنوعی
#machinelearning #ArtificialIntelligence #DeepLearning
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
RNN and LSTM Networks Tutorial Part 1
#منابع #فیلم #دانشگاه #الگوریتمها #کلاس_آموزشی #یادگیری_ماشین #هوش_مصنوعی
#machinelearning #ArtificialIntelligence #DeepLearning
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
ConditionalQA is a question answering dataset that contains complex questions with conditional answers, i.e. the answers are only true when certain conditions apply.
It can motivate doing research for complex question answering over long documents.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
https://paperswithcode.com/dataset/conditionalqa
#مقاله #دیتا #دیتاست
❇️ @AI_Python
It can motivate doing research for complex question answering over long documents.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
https://paperswithcode.com/dataset/conditionalqa
#مقاله #دیتا #دیتاست
❇️ @AI_Python