Gen AI Team

Advanced recursive and follow-up retrieval techniques greatly improve RAGs and solving half of a problem. Chaining them together further enhances results.

TL;DR: I made a GitHub assistant using LLM, RAG, Gemini, and Redis that can help with user issues in repositories.

"Speed up AI/ML model training by using custom operators. These allow for faster processing and improved performance. With the ability to customize operations, developers can optimize their models for specific tasks and achieve more accurate results in less time."

LOESS is a statistical method for fitting curves to data points. A Rust library has been created to implement LOESS, allowing for fast and efficient curve fitting. This library can be used for various purposes such as data analysis and machine learning. Try it out for accurate curve fitting in your projects!

The Poisson Process The Poisson Bootstrap is a statistical method for estimating uncertainty in data sets. It involves randomly resampling data points to create multiple datasets and calculating statistics from each one. The Poisson Process is a mathematical model used to describe the random occurrence of events over time. It is based on the assumption that events occur independently and at a constant rate. Both methods are commonly used in data analysis and can provide valuable insights into patterns and trends.

New training method for physical artificial neural networks could lead to more versatile, scalable, and energy-efficient AI systems using light waves.

"Learn how to create a user insights-gathering tool from scratch to help product managers make data-driven decisions. This step-by-step guide covers everything from defining user needs to implementing feedback collection methods. Streamline your product development process with this practical resource."

For aspiring data scientists: 1. Learn programming and statistics 2. Gain practical experience through internships or projects 3. Keep learning and stay updated with new technologies 4. Prepare for a competitive job market 5. Don't underestimate the importance of communication skills. As a data science student, be ready for a challenging but rewarding journey.

TL;DR: Prepare for data analyst interviews by practicing with AI. Use online resources and mock interviews to improve technical skills and boost confidence. Focus on problem-solving and communication skills to ace the real interview.

"4 visualization libraries that work with Pandas dataframe and use its plotting backend for easy plotting. They are Matplotlib, Seaborn, Plotly, and Altair. Each has its unique features and can produce high-quality visuals with minimal coding. Try them out to enhance your data analysis!"