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Rajesh Srivastava on Instagram: "Neural networks need activation functions because without them, stacking layers does nothing useful. a. Without activations, the entire network collapses into a single linear equation, no matter how deep it is. b. purely linear model cannot learn complex patterns or relationships in data. c. This means no image recognition, no speech understanding, no language modeling, and no real AI. d. Activation functions introduce non-linearity*, which is the key ingredient
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Dev Anand on Instagram: "Various Activation Functions used in Neural Networks #machinelearning #artificialintelligence #mathematics #computerscience #programming"
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