Machine Learning projects to boost your portfolio 🚀

🧵 👇

1️⃣ mRNA Degradation using Deep Learning:

Check this out 👇
https://t.co/1gixtvBxjk
2️⃣ The Self Driving car project:

Check this out 👇
https://t.co/gAgxUhMNjK
3️⃣ Stock Price Forecasting using Deep Learning:

Check this out 👇
https://t.co/0LMOul3ovp
4️⃣ Build a Conversational AI Bot:

Check this out 👇
https://t.co/Uquwzkr2AD
5️⃣ Automatic Speech Recognition:

Check this out 👇
https://t.co/sy3FkbexzY
6️⃣ Image captioning:

Check this out 👇
https://t.co/CJkdAYnzuR
7️⃣ MLOps Zoomcamp: End2End ML project:

This one is my favourite 🤩

1. Model building and training
2. Experiment tracking
3. Orchestration
4. Model deployment
5. Cloud computing
6. Model Monitoring

Check this out 👇
https://t.co/66ni0iWPQH
That's all for today!
Hope you enjoyed reading!! 📖

I’m from India 🇮🇳
Trying to simplify Python, Maths and Machine Learning for you.

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More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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One of the most successful stock trader with special focus on cash stocks and who has a very creative mind to look out for opportunities in dark times

Covering one of the most unique set ups: Extended moves & Reversal plays

Time for a 🧵 to learn the above from @iManasArora

What qualifies for an extended move?

30-40% move in just 5-6 days is one example of extended move

How Manas used this info to book


Post that the plight of the


Example 2: Booking profits when the stock is extended from 10WMA

10WMA =


Another hack to identify extended move in a stock:

Too many green days!

Read
1/ Here’s a list of conversational frameworks I’ve picked up that have been helpful.

Please add your own.

2/ The Magic Question: "What would need to be true for you


3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.

“Gun to the head—what would you decide now?”

“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”

4/ Other Q’s re: decisions:

“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”

“What’s end-game here?”

“What does success look like in a world where you pick that path?”

5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:

“What would the best version of yourself do”?