Backpropagation calculus | Deep Learning Chapter 4
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Backpropagation calculus | Deep Learning Chapter 4
Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to share the videos. Special thanks to these supporters: http://3b1b.co/nn3-thanks Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks This one is a bit more symbol-heavy ...
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AI | Business | Technology on Instagram: "Geoffrey Hinton — often called the Godfather of AI — received the 2024 Nobel Prize for pioneering neural networks and backpropagation, the foundations of modern deep learning. But his acceptance speech was far from celebratory. Hinton warned that AI is already fueling online echo chambers, enabling mass surveillance, and driving large-scale phishing attacks. He cautioned that far greater risks are emerging: AI-generated viruses, autonomous weapons, and d
AI | Business | Technology on Instagram: "Geoffrey Hinton — often called the Godfather of AI — received the 2024 Nobel Prize for pioneering neural networks and backpropagation, the foundations of modern deep learning. But his acceptance speech was far from celebratory. Hinton warned that AI is already fueling online echo chambers, enabling mass surveillance, and driving large-scale phishing attacks. He cautioned that far greater risks are emerging: AI-generated viruses, autonomous weapons, and d
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Artificial Intelligence | AI on Instagram: "The chain rule is a basic idea from calculus that helps us understand how changes in one variable affect another through a chain of functions. In simple terms, if one thing affects a second thing, and that second thing affects a third, we can multiply the rates of change (derivatives) to find the overall effect. This is especially useful in artificial neural networks (ANNs), where each layer transforms its input and passes it to the next. During traini
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Artificial Intelligence | AI on Instagram: "The chain rule is a basic idea from calculus that helps us understand how changes in one variable affect another through a chain of functions. In simple terms, if one thing affects a second thing, and that second thing affects a third, we can multiply the rates of change (derivatives) to find the overall effect. This is especially useful in artificial neural networks (ANNs), where each layer transforms its input and passes it to the next. During traini
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