Reading a research paper and trying to understand what exactly the authors trying to implement give deeper understanding of the topic. In general if you read a blog or some article on that topic it just touches the top layer but when you read a research paper and a good one you

https://t.co/N7G4jGmsvy

We really need a new architectural design. To take LLMs it to new heights. https://t.co/IbgKqgWZk0

Ever tried asking ChatGPT , "Based on our conversational history do you thing I`m dumb?" I`m already crying!

Set Cover problem - Constraint optimisation algorithm can be used to optimise.

I hope everyone should realize how important it is to implement ML algorithms from scratch. Just using @scikit_learn fit method won't teach you anything. Write you algorithm from scratch understand how exactly its behaving. #MachineLearning #Python

Just implemented the Facility Location function to enhance active learning in my optimization library! What does it do? It helps select the most informative data points for annotation, reducing costs & improving model performance. 🔜 More features coming soon! Check it out and

I'm still not able to digest Why would someone who is the richest person in the world talking about making a country great again. Am I missing on something here ?

4/ If you're interested in an open-source library optimized for performance, check out Optimiz on GitHub! I’m constantly adding new algorithms and improvements. Feedback and contributions are welcome! https://t.co/zC2XRuknih #Python #DataScience #Optimization #AI #opensource

3/ My Optimiz Library In #Optimiz, I implemented Cython versions of both Gradient Descent and SGD. Results? Significantly fasterperformance than standard Python! This means more efficient optimization for ML practitioners and researchers.

2/ Why does speed matter in ML? For algorithms that iterate over large datasets—like Gradient Descent or Stochastic Gradient Descent—every second counts. Faster computations mean faster training and quicker insights.

1/ What’s Cython? Cython is a superset of Python that compiles Python code into C, giving you the simplicity of Python and the speed of C. This makes it ideal for performance-critical parts of any #MachineLearning project.

🧵 Do you know about #Cython? Most people don’t realize that libraries like scikit-learn use Cython under the hood to speed up operations like matrix multiplication. 🚀 Python is powerful, but for heavy computations, it needs a little boost. Here’s how Cython makes it happen:

🚀 Launching Optimiz: A New Python Library for Optimization! 🚀 Built #Optimiz from scratch to bring powerful optimization algorithms to ML practitioners! Dive into: 🔥 Gradient Descent, SGD, Nesterov 🔥 Newton’s Method, Proximal & Coordinate Descent 🔜 Plans: Active Learning,

I thought I understand all the algorithm related to sub modular optimization in Machine Learning and ready to add`em in my library and then I came across @jmschreiber91 `s apricot. I don't think I`ll be able to write a library that matches level of apricot. Its so overwhelming.

https://t.co/zC2XRuknih

🚀 New Update! 🚀 We've just Cythonized the SGD algorithm in #Optimiz, making it faster than ever for large-scale #MachineLearning optimization tasks! 🚀💻 Check out the code and speed boost: [GitHub Link] #Python #Cython #ML #DeepLearning #AI #Optimization #opensource

🚀 Excited to announce Optimiz my Python library for convex optimization algorithms! 🎯 If you're looking for a toolkit to solve complex optimization problems from scratch, check it out on GitHub! 👉 https://t.co/zC2XRuknih #MachineLearning #Optimization #Python #OpenSource #AI

Just checked in my code for co-ordinate Gradient Descent on #GitHub . I am currently writing a library in python for optimization algorithms. Its fun to code this algorithms from scratch. #Python #MachineLearning https://t.co/zC2XRuknih

https://t.co/QejhYcUCGI #artificalintelligence #GitHub