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neuralnetworksanddeeplearning.com | ||
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towardsdatascience.com
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| | | | | Learn how to build feedforward neural networks that are interpretable by design using PyTorch. | |
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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] The article provides an in-depth explanation of how to build a neural network from scratch, focusing on the implementation of a simple average function and the introduction of activation functions for non-linear tasks. It discusses the use of matrix operations, the importance of GPUs for acceleration, and the role of activation functions like ReLU. The author also outlines next steps for further exploration, such as expanding the model, adding layers, and training on datasets like MNIST. | |
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kevinlynagh.com
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| | | [AI summary] The author discusses their experience developing a simple neural network for sensor data processing on a microcontroller, highlighting challenges with quantization and inference optimization. | ||
