Master the six core AI architectures—RNNs, LSTMs, CNNs, Transformers, GANs, and GNNs. Learn when to use each and make informed decisions for your AI projects.
Start ReadingWhy are there so many different AI models? Understanding the big picture before diving into specifics.
How Recurrent Neural Networks introduced the concept of memory in neural networks for sequential data.
How Long Short-Term Memory networks fixed RNN limitations with gates and cell states.
The architecture that revolutionized AI. Understanding attention mechanisms and why transformers dominate language models.
The architectures built for visual data. Understanding when to analyze images versus generate them.
When your data has connections and relationships, GNNs are your specialized tool.
The decision framework. A side-by-side comparison to help you choose the right AI architecture for your specific problem.