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kavita-ganesan.com | ||
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programminghistorian.org
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| | | | | [AI summary] The text provides an in-depth explanation of using neural networks for image classification, focusing on the Teachable Machine and ml5.js tools. It walks through creating a model, testing it with an image, and displaying results on a canvas. The text also discusses the limitations of the model, the importance of training data, and suggests further resources for learning machine learning. | |
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neuralnetworksanddeeplearning.com
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| | | | | [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens... | |
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www.v7labs.com
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| | | | | A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they work. | |
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www.lesswrong.com
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| | | A neural net using rectified linear unit activation functions of any size is unable to approximate the function sin(x) outside a compact interval. ... | ||
