Gen AI Team

TL;DR: This blog compares three small-scale AI models, Gemma, Llama, and Mistral, to see how well they understand and answer questions. Gemma, the smallest,...

TL;DR: Learn how to use NumPy and NetworkX in Python to represent and visualize network data. This tutorial will guide you through the process of creating and visualizing graphs in an easy and practical way.

"TL;DR: Data scaling involves transforming data to a specific range for better analysis. Standardization and Min-Max scaling are common methods, with MinMaxScaler being better for uniform data and StandardScaler for normally distributed data. Other methods may be suitable for specific data types or situations."

TL;DR: This article explains how stochastic regularization can improve entity embeddings, and explores how neural networks process categoricals and their hierarchies. It provides visualizations to help understand these concepts.

Learn how to use Python for causal inference, specifically propensity score matching and estimating treatment effects in non-randomized settings. Includes step-by-step examples and Python code.

Multi-Agent-as-a-Service (MAaaS) is a software system that allows multiple agents to work together in a coordinated manner. It simplifies the development and deployment of multi-agent systems, making it easier for senior engineers to implement and manage. This service offers a range of benefits, including increased efficiency and improved collaboration among agents.

TL;DR: Learn essential techniques for managing large amounts of data in Hive and HQL. Use PARTITIONED BY, STORED AS, DISTRIBUTE BY/CLUSTER BY, and LATERAL VIEW with EXPLODE and COLLECT_SET for efficient data handling.

To master Large Language Models (LLMs), start by learning basic NLP with tools like NLTK. Progress to understanding word embeddings with Word2Vec, explore text classification and generation methods, and finally, delve into Attention mechanisms and Transformers. This foundational knowledge will empower you to work effectively with LLMs and innovate in the field.

"Improving LLM query generation and dynamic few-shot prompting can be achieved through a simple strategy. This involves using a language model and prompts to enhance the ability to generate relevant queries. By fine-tuning the language model and creating targeted prompts, the overall performance of LLM can be significantly improved."

A dummy classifier is a simple algorithm used in machine learning for beginners. It assigns classes based on the majority class in the training data, making it easy to understand and implement. This visual guide includes code examples to help beginners grasp the concept easily.