What is explainable AI? Use cases, benefits, models, techniques and principles
Explainable AI refers to a collection of processes and techniques that enable humans to comprehend and trust the outputs generated by machine learning algorithms.
How to use LLMs in synthesizing training data?
Harnessing the power of large language models (LLMs), a mighty tool capable of understanding, generating, and even refining human-like text we can generate synthesized training data that is flawless and train our models more efficiently.
Pattern recognition in ML: A comprehensive overview
Pattern recognition in machine learning enables systems to discover similarities and automatically extract meaningful information from a given dataset.
Data security in AI systems: An overview
As AI continues to disrupt numerous business sectors, the importance of data security in AI systems becomes increasingly critical.
AI model security: Concerns, best practices and techniques
The potential for misuse or malicious attacks grows as we increasingly rely on AI systems to make vital decisions. Therefore, a strong security framework that safeguards against such threats while enabling the beneficial use of AI is necessary.
Data analysis: Its importance, types, process, workflow and tools
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision making.
