یه بلاگ دارای محتویات nlp دار و بیگ دیتا
پایینم صرفا قسمت معرفی بلاگرشه
About | Sijun He

Looks like somebody has stumbled upon my blog!

My name is Sijun He. I am currently a machine learning engineer at Twitter Cortex focusing on Deep Learning and NLP for content understanding. Before that, I was a data scientist at Autodesk, where I did analytics & data mining on products(i.e. AutoCAD, Revit, Maya, etc.) that help engineers design towering skyscrapers and filemakers craft Oscar-winning visual effects.

The blog documents my journey of learning data science from scratch. It will mostly be reading notes and personal projects I have done for work, school or fun. The blog is a journal for myself. Whenever I am upset about not making enough progress (happens ALL THE TIME), I read my blog and remind myself of how far I have gone. If you like the blog, I encourage you to have one for yourself.

https://sijunhe.github.io/

مطالب جالبی تو این بلاگ پیدا میشه
به شدت توصیه می کنم

https://peterroelants.github.io/

Get full Access to all CloudxLab Self-Paced Courses till 30th April

https://cloudxlab.com/home

از خیره شدن به مانیتورتون برای پایان آموزش مدل خسته شدید؟

𝗺𝗹𝗻𝗼𝘁𝗶𝗳𝘆 یه پکیج خیلی ساده منبع باز پایتونه که با یه خط ساده کد زدن، بهتون یه QR میده، شما اسکنش می‌کنید و منتظر می‌مونید تا اتفاقاتی که در طول آموزش مدل می‌افته رو از طریق نوتیفیکیشن گوشی‌تون دریافت کنید!!!

اول: import mlnotify
دوم: کد آموزش مدل رو مثل همیشه بنویسید و اجرا کنید
سوم: کد QR تولید شده توسط پکیج mlnotify رو اسکن کنید.
حالا از آب و هوای خوب لذت ببرید و اجازه بدید تلفن هوشمندتون بهتون بگه چه زمانی تمرین تموم شده

شما هم چک کنید:

https://github.com/aporia-ai/mlnotify

@Ai_Events

این 6 کتاب کاملا رایگان هستند:

1- Deep Learning
(by ian goodfellow et al)
2- Dive into Deep Learning
(by Zhang et al)
3- Machine Learning Engineering
(by Andriy Burkov)
4- Python Data Science Handbook
(by Jake VanderPlas)
5- Probabilistic Machine Learning
(by Kevin Patrick Murphy)
6- Machine Learning Yearning
(by Andrew Ng)

توضیح مختصر کتاب ها:
https://t.iss.one/cvision/2567
@Cvision

https://t.iss.one/cvision/2566

1) A nice book for machine learning and deep learning foundations

2) Good balance between theories and practice(uses multiple tools, NumPy/MXNet, PyTorch, and TensorFlow)

3) A nice overview of machine learning workflows. Very brief and concise.

4) A nice one for getting started with the most important data science tools - IPython, NumPy, Pandas, Matplotlib(and Seaborn), Scikit-Learn

5) Best for theoretical concepts

6) A nice book for learning how to structure machine learning projects and perform error analysis in the right way.

A generalist agent

مقاله جدید deepmind که یه agent ای ارایه دادن که با یک شبکه و وزن های یکسان می‌تونه هم آتاری بازی کنه هم برای تصاویر کپشن تولید کنه هم چت کنه و چند تا کار دیگ 🔥🔥🔥


Abstract

Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato

Link: https://www.deepmind.com/publications/a-generalist-agent

آنالیز تصاویر فعالیت مغز انسان با استفاده از ابزارهای یادگیری ماشین

سخنران:دکتر محمدیوسف نژاد، پژوهشگر پسا دکترای دانشگاه آلبرتا

این سمینار علاوه بر اطلاعات تخصصی، اطلاعات عمومی خوبی نیز برای دانشجویان دوره کارشناسی خواهد داشت.

زمان: یکشنبه 8 خرداد ساعت 17:30

لینک ثبت نام رایگان:
https://evnd.co/FFxPk

Don't try to reinvent the wheel when approaching a new problem with machine learning or data science. How to carry out literature review effectively in an area of AI that you aren't super familiar with?

1. Start with large-scale review papers. They provide an updated overview of a field. Example: Two years ago, I wanted to refresh my knowledge of adversarial attacks and defenses. I googled "Adversarial attacks in ML review paper" and came across this review paper: https://lnkd.in/g3m6vdVy (Wiyatno et al.). It summarized recent advances effectively for me. Tip: Google "<your topic> review paper".

2. Call up a friend who knows better about the task at hand, whenever possible. They are probably busy, so just ask them for 5-10 resources (papers, blogs, talks, etc.). Example: In 2017, I was building a trigger word detector with Andrew Ng. It was my first end-to-end exposure to a speech problem. One day in the CS department at Stanford, I ran into Awni Hannun. He's one of the world's expert in speech recognition. I pitched him my problem and in 5 minutes, he was able to provide me with the resources on the most recent models, open-source repositories, hyperparameter tuning tricks, and normalization methods. It probably saved me months of work.

3. Read the introduction of papers. It's typically the section right after the abstract that narrates prior work and links to seminal papers of the field. You can then find these papers and go down the chain (by reading their introductions) to better understand the field you're delving into. Tips: It's not a perfect signal, but you can look for the number of citations of a paper (assuming it has been around for long enough) on Google Scholar as a noisy proxy for trust.

Hope it helps!

https://www.linkedin.com/posts/kiankatan_dont-try-to-reinvent-the-wheel-when-approaching-activity-6935248113704087552-Wzv9?utm_source=linkedin_share&utm_medium=android_app

It will also be free to use for verified students and maintainers of popular open source projects. Get your Student-Pack now!

https://github.blog/2022-06-21-github-copilot-is-generally-available-to-all-developers/#:~:text=We're%20making%20GitHub%20Copilot,of%20popular%20open%20source%20projects.