Pedro Magalhães

PhD Canditate @CInUFPE @etsmtl @MIla_Quebec. Applied Scientist Intern @amazon. Unsupervised, self-supervised representation Learning; Opinions are my own.

[Yann LeCun @ylecun]

Another lecture of NYU Deep Learning, Spring 2021is available.
@alfcnz gave me magic fingers.

[Heitor Rapela @HeitorRapela]

Join us today for the #LatinXinAI @_LXAI research workshop at @icmlconf!

@pedro_mbraga talking about the AI Research Mentoring Program @_LXAI.

#ICML #ICML2021 #MachineLearning #DiverseInAI

[Pedro Magalhães @pedro_mbraga]

@CapitalOne Diversity in AI Summit is starting soon. Join us! @_LXAI #LatinXinAI

https://t.co/DMLhm8DdIp

[accelai @AccelerateAI]

Join the Capital One’s Diversity in AI summit, this July 14, 2021, at 12:00 PM EST - 4:00 PM EST. Register here: https://t.co/vS2KdOJkHv

https://t.co/SLIc86wWwL

[Dr. Matias Valdenegro Toro @mvaldenegro]

Mentoring hour starting soon!

[LatinX in AI (LXAI) @_LXAI]

LIVE NOW!! Our second #keynote for #LatinXinAI at @iclr_conf Pedro Braga @pedro_mbraga sharing a presentation on Towards Learning Interpretable Representations of Perception #ICLR2021

[Pablo Samuel Castro @pcastr]

📢Announcing MUSICODE!📢
🎶💻🎨
this is a youtube channel where in each episode i'll explore a topic in computer science💻, a topic in music🎶, and combine them in creative ways 🎨.

**first episode coming this friday!**

**subscribe here:**
https://t.co/VhFvC3xgpz

[Kyunghyun Cho @kchonyc]

students are curious, and their questions are great: a few select questions from this semester, and my (incomplete) answers at https://t.co/idiHOnOQTs

[Pedro Magalhães @pedro_mbraga]

Another great event! It was a pleasure to be part of it.

[LatinX in AI (LXAI) @_LXAI]

Thanks to all the mentors who joined us for the #LatinXinAI Mentoring hour and for their continued support in providing their experience and expertise to new LXAI members! 👏

[CIn-UFPE @CInUFPE]

Tese de professor e dissertação de aluno do CIn-UFPE são premiadas do CTDIAC da BRACIS. Saiba mais: https://t.co/KemG76TiB9 #OrgulhoCIn

[Sergey Levine @svlevine]

My deep RL course (CS285) now has fall 2020 lectures online, here: https://t.co/Y674PBH6TS

We'll update this each week with the latest lectures. Hopefully these lectures are helpful! We tried to update material from past years, and recorded it in a more online-friendly format.

[The Nobel Prize @NobelPrize]

BREAKING NEWS:
The Royal Swedish Academy of Sciences has decided to award the 2020 #NobelPrize in Physics with one half to Roger Penrose and the other half jointly to Reinhard Genzel and Andrea Ghez.

[Santiago @svpino]

Python 3.9 🐍 is out! 🥳

Here are the 5 new features you care about.

🧵👇

[LatinX in AI (LXAI) @_LXAI]

Our mentorship program is open until Oct 16th! Are you looking to give back what you have learned? Become a mentor! Sign in here: https://t.co/7lrtZDR7zO
Do you need some guidance to continue your AI career? Apply as a mentee here: https://t.co/K8Rd0PZTot
Photo by F. Romero

[Laura N Montoya @QuickResolute]

Excited to open applications for #LatinXinAI Mentorship Program!

Proud to serve 80 mentees during our pilot, increasing #AI research capabilities, improving grad school applications, & advising on career advancement.

Apply to be a Mentor or Mentee today!

@_LXAI @AccelerateAI

[Andrew Ng @AndrewYNg]

@goodfellow_ian, @AnimaAnandkumar, Alexei Efros, @realSharonZhou and I will be speaking at GANs for Good, an online panel, on September 30th. This is to celebrate the launch of @DeepLearningAI_’s new GANs Specialization. Come join us! https://t.co/BxuwK4uPvR

[NumPy @numpy_team]

The NumPy paper is out!

https://t.co/q2Svekk9G8

[Dr. Ganapathi Pulipaka 🇺🇸 @gp_pulipaka]

Why is Machine Learning So Magical? #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/S5Ed0J3ph8

[Brady Neal @CasualBrady]

In this week of the Introduction to Causal Inference online course, we enter @yudapearl land: *graphical models*.

Writing down a causal graph is a concise, intuitive way to express our causal assumptions. Video lectures, slides, and reading online at https://t.co/1dRV4l5eM0. 1/2