انتشار دوره کلاس آموزشی دانشگاه Purdue
Machine Learning For Cyber Security

#یادگیری_ماشین #منابع #کلاس_آموزشی

🔰 @AI_Python

Natural Language Processing from Stanford University: Distilled Notes

👉🏼 http://nlp.aman.ai
#منابع

🔰 @AI_Python

The Complete Collection of Data Science Projects

https://www.kdnuggets.com/2022/08/complete-collection-data-science-projects-part-1.html

#معرفی سایت

تو این سایت می‌تونید یه کلمه یا collocation تو انگلیسی را سرچ کنید، و براتون توی روزنامه ها و سایتهای معروف میگرده و هر جا این عبارت استفاده شده را میاره،
یه کاربرد عالیش برای پیدا کرد مثالهای واقعی استفاده از یک اصطلاح یا لغته:

https://ludwig.guru/

منابع رایگان شروع هوش مصنوعی

Artificial Intelligence for Beginners
https://github.com/microsoft/ai-for-beginners

MindMap
http://soshnikov.com/courses/ai-for-beginners/mindmap.html

Description
https://www.kdnuggets.com/2022/08/free-ai-beginners-course.html

#منابع #فیلم #هوش_مصنوعی #کلاس_آموزشی #کتاب

🔰 @AI_Python

گروه DeepLearning and AI

https://t.me/DeepLearningAIExperts

گروه پردازش زبان طبیعی NLP:

https://t.me/NLPExperts

گروه زبانهای برنامه نویسی پایتون و لینوکس و...

https://t.me/PythonLinuxExperts

کانال گروه :
❇️ @AI_Python

دوره کلاسی جدید دانشگاه MIT
MIT Deep Learning for Art, Aesthetics, and Creativity

Generating photorealistic images and arts has been the highlight of AI in 2022.
Covering AI + creativity, GANs, diffusion models, etc.

Videos: https://youtube.com/playlist?list=PLCpMvp7ftsnIbNwRnQJbDNRqO6qiN3EyH

Website: https://ali-design.github.io/deepcreativity/

#منابع #فیلم #کلاس_آموزشی #یادگیری_عمیق
#DeepLearning

❇️ @AI_Python

Ghost-free High Dynamic Range Imaging with Context-aware Transformer

Github: https://github.com/megvii-research/hdr-transformer

Paper: https://arxiv.org/abs/2208.05114v1

Dataset: https://cseweb.ucsd.edu/~viscomp/projects/SIG17HDR/

Harvard CS109A #DataScience course materials — huge collection free & open!

1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

Google engineers offered 28 actionable tests for #machinelearning systems. 👇

Introducing 👉 The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction (2017). 👈

If #ml #training is like compilation, then ML testing shall be applied to both #data and code.

7 model tests

1⃣ 👉 Review model specs and version-control it. It makes training auditable and improve reproducibility.

2⃣ 👉 Ensure model loss is correlated with user engagement.

3⃣ 👉 Tune all hyperparameters. Grid search, Bayesian method whatever you use, tune all of them.

4⃣ 👉 Measure the impact of model staleness. The age-versus-quality curve shows what amount of staleness is tolerable.

5⃣ 👉 Test against a simpler model regularly to confirm the benefit more sophisticated techniques.

6⃣ 👉 Check the model quality is good across different data segment, e.g. user countries, movie genre etc.

7⃣ 👉 Test model inclusion by checking against the protected dimensions or enrich under-represented categories.

7 data tests

1⃣ 👉 Capture feature expectations in schema using statistics from data + domain knowledge + expectations.

2⃣ 👉 Use beneficial features only, e.g. training a set of models each with one feature removed.

3⃣ 👉 Avoid costly features. Cost includes running time, RAM as well as upstream work and instability. 

4⃣ 👉 Adhere to feature requirements. If certain features can’t be used, enforce it programmatically.

5⃣ 👉 Set privacy controls. Budget enough time for new feature that depends on sensitive data.

6⃣ 👉 Add new features quickly. If conflicting with 5⃣ , privacy goes first.

7⃣ 👉 Test code for all input features. Bugs do exist in feature creation code.

See 7 Infrastructure & 7 monitoring tests in paper. 👇

They interviewed 36 teams across Google and found

👉 Using a checklist helps avoid mistakes (like a surgeon would do).

👉 Data dependencies leads to outsourcing responsibility. Other teams’ validation may not validate your use case.

👉 A good framework promotes integration test which is not well adopted.

👉 Assess the assessment to better assess your system.
https://research.google.com/pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf

✔️ چرا اپل تنها یک شرکت تکنولوژی نیست؟

#business
#کسب‌وکار

@Theeconomicspapers

چالشها و چشم اندازهای هوش مصنوعی و یادگیری ماشین
❇️ @ai_python

#هینتون : یک مثال عملی برای RNN
@ai_python

Why google Bert was ahead of it's time?

Because it was a masked language model even before COVID
😂😂😂😂😂

@ai_python

10 Free Resources To Learn PyTorch In 2022

List of curated PyTorch resources:

1) PyTorch Official Tutorials.

2) Intro to Deep Learning with PyTorch by Facebook AI.

3) PyTorch Fundamentals By Microsoft

4) PyTorch - Python Deep Learning Neural Network API by Deeplizard.

5) Deep Neural Networks with PyTorch by Joseph Santarcangelo

6) PyTorch Basics for Machine Learning by IBM

7) Deep Learning with Python and PyTorch

8) Pytorch - Deep learning with Python by Harrison Kinsley, Sentdex

9) Make Your First GAN Using PyTorch

10) PyTorch Tutorials By Morvan Zhou

Bonus:
Deep Learning with PyTorch book📚

#فیلم #کلاس_آموزشی #هوش_مصنوعی #الگوریتمها

❇️ @AI_Python

کانال ما حامی بنیاد نیکوکاری نسیم مهربانی هست . علاقمندان به شرکت در این مهربانی در حدی که علاقه دارند کمک کنید