Shivagowri

I pursue multiple things.

[Shivagowri @thats_shiv19]

How cool it is being a bird, you can just free fall, fly up to the sky, run on water.

[Techmeme @Techmeme]

Girls In Tech, Women Who Code, and other groups that aimed to diversify Silicon Valley are closing or rebranding to stay afloat, as critics attack DEI efforts (Washington Post)

https://t.co/gAXIOjt2v3

📫 Subscribe: https://t.co/OyWeKSRpIM
https://t.co/zEVHK0IDak

[Peter Yang @petergyang]

"You can have a great strategy for the next billion-dollar developer product, but the battle is won in the trenches of daily iterations."

I love this clip from Lee Robinson (VP Product, Vercel). @leeerob helped scale Vercel to 1M+ developers and has some of the clearest thinking of developer marketing that I've read.

We talked about:

- Getting into the developer's mindset
- 3 pillars of a great developer experience
- Why developers don't trust your marketing

A few quotes from Lee:

"Most developers don't adopt a tool the first time they hear about it. It's probably the 10th time."

"Great documentation is great writing. The tech industry is plagued with jargon and fluff. It would be much cleaner if people wrote more like how they talk."

"The battle is won in the trenches. It could be a two-paragraph docs change or a small UX improvement. For every developer that complains, 100 more won't say anything."

📌 Watch now: https://t.co/IENokWJEUw

[Santiago @svpino]

This repository gives you virtually infinite FREE Computer Science Education:

"Computer Science Courses With Video Lectures"

Enjoy!

https://t.co/0LGgKPMa4S

[Charly Wargnier ♨️ @DataChaz]

🤗 Woohoo!! @huggingface's new Image-to-Text pipeline is out!

[François Chollet @fchollet]

Those who want change take steps towards it, no matter how small. Those who are only pretending to want change make excuses.

[The Full Stack @full_stack_dl]

FSDL Lecture 2: Development Infrastructure & Tooling is now live!

We cover what you need to know about:
• software engineering
• deep learning frameworks
• distributed training
• GPUs (cloud and on-prem)
• experiment management

(Link below)

[javinpaul @javinpaul]

100+ data structure algorithms problems
1. Array - https://t.co/tXqrvpJO3S
2. String - https://t.co/tXqrvpJO3S
3. Binary tree - https://t.co/fsi40ENQRL
4. Linked List - https://t.co/iuItMZrvTA
5. Dynamic Programming - https://t.co/qYKjmZseXr
6. Recursion - https://t.co/4R3eODO4iL

[Akshay 🚀 @akshay_pachaar]

5️⃣ AI/ Machine Learning blogs that you should follow. ✅

🧵 👇

[Sayak Paul @RisingSayak]

.

@DeepMind released this GOLD a couple days back. If you ever wanted to study Transformers from scratch I think this would be that one resource you wouldn't want to miss:

https://t.co/ORtQAinlUj

[Aishwarya Nevrekar @nevrekaraishwa2]

Machine Learning Projects with source code
in Python😲

A thread 🧵👇

[Darek Kłeczek @dk21]

This week we’ve wrapped up @wandb Effective MLOps - Model Development course and had some of our participants present their final project reports - it was a blast! Here are some of the amazing projects that were presented: 1/7

[Jean de Nyandwi @ ✈️🌁COLM 26 @Jeande_d]

ConvNets reformed the state of computer vision a while back. Vision Transformers are also doing it now.

Here is a well-written blog post that provides a breadth-overview of transformers in various visual recognition tasks.

https://t.co/7yDVQ5Wmv9

[Santiago @svpino]

Many people new to machine learning have no idea that labeling data is a problem they need to think about.

To be clear: "labeled datasets" aren't a thing in the real world.

Here is an excellent approach to getting past this problem:

1 of 11

[Andrej Karpathy @karpathy]

Enumerated and sorted some sci-fi I've read over time https://t.co/e0NvnKfwt6 seeking more favorites!

[Shivagowri @thats_shiv19]

@PereddiTarun 👏👏

[Santiago @svpino]

Here is what Machine Learning tutorials told you to do:

1. Start by transforming your dataset
2. Then split it (train, validation, and test sets)
3. Finally, build your model

Please, unlearn this process. There's a problem with it:

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[Jean de Nyandwi @ ✈️🌁COLM 26 @Jeande_d]

First Principles of Computer Vision, Columbia University

Really nice lectures on the physical and mathematical foundations of computer vision.

140 videos that you can watch at your pace. Slides are also provided to follow along.

https://t.co/GqPlV2SzSU

[Marta 🥷 @DatosNinja]

Are you able to recognise if your Machine Learning model is telling you BS or not?

There's only one way: Exploratory Data Analysis (EDA) aka "get to know your data"

Some stuff you could be doing with your data 🤹‍♀️

A thread 🧶 [↓]

[Mike Tamir, PhD @MikeTamir]

“A place to collaborate and share course notes on all topics related to machine learning, NLP, and AI.” https://t.co/V3OmjEdQBh
#AI #DeepLearning #MachineLearning #DataScience