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natureofcode.com | ||
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ujjwalkarn.me
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| | | | | An Artificial Neural Network (ANN) is acomputational modelthat is inspired by the way biological neuralnetworks inthe human brain process information. Artificial Neural Networks have generated a lot ofexcitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to... | |
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matt.might.net
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| | | | | [AI summary] This text explains how a single perceptron can learn basic Boolean functions like AND, OR, and NOT, but fails to learn the non-linearly separable XOR function. This limitation led to the development of modern artificial neural networks (ANNs). The transition from single perceptrons to ANNs involves three key changes: 1) Adding multiple layers of perceptrons to create Multilayer Perceptron (MLP) networks, enabling modeling of complex non-linear relationships. 2) Introducing non-linear activation functions like sigmoid, tanh, and ReLU to allow networks to learn non-linear functions. 3) Implementing backpropagation and gradient descent algorithms for efficient training of multilayer networks. These changes allow ANNs to overcome the limitations of ... | |
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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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sirupsen.com
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| | | [AI summary] An educational guide explaining how to build a neural network from scratch using Python, covering concepts like layers, gradient descent, autograd, and activation functions. | ||
