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AI Deconstructed
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EP14 - Activation Functions: The Spark of Non-Linearity in Neural Networks

44 min•16 augusti 2025
Why can a 100-layer neural network be no smarter than a single neuron? The answer lies in linearity. This episode deconstructs activation functions, the essential components that introduce non-linearity and allow networks to learn complex patterns. We explore the journey from the classic Sigmoid and Tanh functions, diagnose their career-ending "vanishing gradient" problem, and crown the modern champion: ReLU.

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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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