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AI Agents | Cloud | ML | Looking for opportunities

[Gaurav Sen @gkcs_]

This is how large language models turn objects to vector representations.

In this video, we explore how large language models (LLMs) convert objects into internal representations, especially when translating between languages like English and Hindi.

Using real-world examples, we highlight the challenges of gender inference, grammatical structure, and why direct word-to-word translations often fail.

If you're curious about how LLMs deal with multilingual contexts and what it takes to improve translation quality across languages, this video is for you.

#LLMs #Vectors #LCM

[Deep @deepstwt]

@browser_use Looks like vibe is real.

[Deep @deepstwt]

@PrajwalTomar_ When it will be ready for demo?

[Deep @deepstwt]

@paulabartabajo_ I think MLOps is future because of LLMs.

[Deep @deepstwt]

@thisiskp_ Damn!!

[Deep @deepstwt]

@EHuanglu Okay so now AI will replace dancers as well?🤔

[Deep @deepstwt]

@paulabartabajo_ Well articulated thank you!

[Deep @deepstwt]

@paulabartabajo_ How?

[Deep @deepstwt]

@natolambert That's why i love open source!!

[Deep @deepstwt]

@RemiCadene Looking promising.

[Deep @deepstwt]

@obanmol10 Can you refer me for this position?

[Deep @deepstwt]

🔍 Dive into the world of CNNs and Discover how padding and pooling solve critical challenges in CNNs.

Ready to unravel the mystery behind these tech marvels? Let’s decode CNNs together! 🧠✨

👉 Read more on my medium article :-
https://t.co/4gV2khkni9

#Article #images

[Deep @deepstwt]

🚗💡 Ever wondered what drives Tesla's autopilot or how Snapchat gives you those cool filters? It's all thanks to Convolutional Neural Networks (CNNs)!

🎯 Did you know Tesla’s AI processes more data in a minute than we do in a year of photos?

#TechMagic #Tesla #CNN #AI #ML

[Deep @deepstwt]

Final Thoughts

CatBoost is a game-changer for machine learning tasks involving categorical data and missing values. Its ability to handle such challenges with minimal preprocessing makes it an indispensable tool in a data scientist's toolkit.

Thank you for your time! I (n/n)

[Deep @deepstwt]

Automatic Handling of Missing Values ❓:No need for complex imputation techniques! CatBoost treats missing categorical values as a separate category and can even split on missing values for numerical data, making it robust in dealing with incomplete datasets.
(6/n)

[Deep @deepstwt]

Blazing Fast Performance on Both CPU and GPU ⚡:CatBoost is designed to be highly scalable and fast, whether you’re running it on a CPU or GPU. This makes it suitable for both large datasets and high-performance applications. (7/n)

[Deep @deepstwt]

CatBoost automatically applies sophisticated methods like mean encoding, combinatorial feature selection, and one-hot encoding. This not only simplifies the preprocessing but also enhances performance. (5/n)

[Deep @deepstwt]

One evening, I was casually scrolling through the list of Kaggle competition winners (link: Winning Solutions of Kaggle Competitions) and to my surprise, I found a common factor boosting many winners’ performances: a Cat! 🐱 Yes, it's CatBoost, (1/n)

[Deep @deepstwt]

Built-in Handling of Categorical Features 🏷️:Unlike many other algorithms, you don't need to manually convert categorical features to numerical ones using techniques like one-hot encoding. (4/n)

[Deep @deepstwt]

What is CatBoost?

CatBoost is a powerful machine learning library used primarily for regression and classification tasks. But why is it so special and widely acclaimed? Here are three key reasons: (3/n)

[Deep @deepstwt]

an elegant algorithm that’s making data scientists purr with delight.

Are you curious to know what this Cat looks like and how it can bring a smile to your face? Let’s dive into the world of CatBoost. (2/n)