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AI Deconstructed
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EP20 - The Transformer Architecture: Attention is All You Need

28 min•27 september 2025
This episode deconstructs the 2017 paper that revolutionized AI. We go "under the hood" of the Transformer architecture, moving beyond the sequential bottleneck of RNNs to understand its parallel processing and the core mechanism of self-attention. Learn how Queries, Keys, and Values enable the powerful contextual understanding that powers all modern Large Language Models.

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EP25 - The Alignment Problem: Ensuring a Safe and Beneficial Future
•43 min
EP24 - AI Ethics: Decoding Algorithmic Bias, Fairness, and Accountability
•34 min
EP23 - Generative AI (Part 2): Diffusion Models and the Art of Denoising
•39 min
EP22 - Generative AI Part 1: GAN and VAE Creative Architectures
•22 min
EP21 - Large Language Models and The Power of Scale
•32 min
EP19 - Robotics and Embodied AI: Giving AI a Body
•40 min
EP18 - LSTMs and the Vanishing Gradient: Solving AI's Long-Term Memory Problem
•33 min
EP17 - Recurrent Neural Networks: How AI Learns to Remember the Past
•45 min
EP16 - The CNN Engine: Inside Convolutional and Pooling Layers
•45 min
EP15 - The Convolutional Revolution: How CNNs Gave AI Vision
•40 min

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