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AI Deconstructed
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EP13 - AI's Learning Engine: Gradient Descent and Backpropagation

42 min•9 augusti 2025
How does a neural network with millions of parameters actually learn from its mistakes? This episode dives under the hood of deep learning's core engine, demystifying the two algorithms that make it all possible: Gradient Descent and Backpropagation. We'll use intuitive analogies to explain how AI navigates a vast mathematical landscape to find the answers that minimize its errors.

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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
EP20 - The Transformer Architecture: Attention is All You Need
•28 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

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