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Neural Networks explained with paint (Art of the Problem)
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#backpropagation#neuralnetworks#machinelearning
How Neural Networks Learn | Backpropogation Intuition
Art of the Problem
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- Recommended by 1 person: 1 in engineering communities.
- May be out of date. Uploaded 6 years ago, in an area where tools change fast.
Beginner. No background in the topic needed.
61K views6 years ago1 recommendation
AI and machine learningComputer scienceNeural networks and deep learningMath for machine learning
An intuitive visual explanation of how backpropagation works to train neural networks by calculating errors and updating weights.
Explore the fundamental process of backpropagation in artificial intelligence (AI). This video show how neural networks learn and improve by adapting to data during each training phase. Backpropagation is crucial in calculating errors and updating the network's weights to enhance decision-making within the AI system. This tutorial breaks down the core mechanics of neural network training, making it easier to understand for individuals interested in AI, machine learning, and network training. By understanding backpropagation, viewers can better grasp how neural networks evolve to process information more accurately. Keywords: rosenblatt, AI, Artificial Intelligence, Neural Networks, Backpropagation, Machine Learning, Network Training, Data Adaptation, Error Calculation, Performance Tuning, Decision Making. Hinton Nobel prizeWhy it's here
1 person recommends thisWhat people say. Recommended as a clear, hand-drawn visual introduction to the intuition behind neural network backpropagation.