How Does Variational Autoencoder Work? Explained!
Variational Autoencoder is a an explicit type generative model which is used to generate new sample data using past data. VAEs do a mapping between latent variables, dominate to explain the training data and underlying distribution of the training data. These latent variables vectors can be used to reconstruct the new sample data which is close to the real data.…
Unveiling RAG: A Powerful Fusion of Retrieval and Generation
Retrieval-Augmented Generation (RAG) is an innovative approach in natural language processing (NLP) that combines the strengths of two different models: retrieval-based models and generation-based models. So to grasp the essence of this topic lets forget about the Retrieval augmented part and focus on the term Generation, this refers to large language models which generate text in response to user query…
How Intelligent Agents Work: PEAS, Environment & Decision-Making Explained
If you’ve ever wondered how self-driving cars navigate complex traffic or how medical diagnosis systems make accurate predictions, you’re looking at intelligent agents in action. These autonomous systems are revolutionizing industries, with the AI agents market projected to reach $50.31 billion by 2030, growing at a staggering 45.8% annually. But how intelligent agents work goes far beyond simple automation. Understanding…
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Understand Confusion Matrix Using Real-life Classification Example
First I’d like to explain what is the use of a confusion…
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Sandeep Kumar is the Founder & CEO of Aitude, a leading AI tools, research, and tutorial platform dedicated to empowering learners, researchers, and innovators. Under...
I'm Erika Balla, a Hungarian from Romania with a passion for both graphic design and content writing. After completing my studies in graphic design, I...
