Titlebook: Convolutional Neural Networks with Swift for Tensorflow; Image Recognition an Brett Koonce Book 2021 Brett Koonce 2021 convolutional neural

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Richard Edwards,Katherine NicollIn this chapter, we will modify our one-dimensional neural network by adding convolutions to produce our first actual convolutional (2D) neural network and use it to categorize black and white (e.g., MNIST) images again.

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Workers, Subjectivity and Decent Work,In this chapter, we will build VGG, a state-of-the-art network from 2014, by making an even larger version of our CIFAR network.

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The Fusarium Head Blight Pathosystem,In this chapter, we will look at a MobileNetV3, which delivers an optimized version of EfficientNet on mobile hardware by reducing the complexity of the network. This model is heavily based on EfficientNet’s search strategy with mobile-specific parameter space goals.

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P. Ruckenbauer,H. Buerstmayr,M. LemmensIn this chapter, we will look at how we can modify our original ResNet 50 network to achieve nearly as accurate of results as EfficientNet by combining many different approaches.

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MNIST: 1D Neural Network,In this chapter, we will look at a simple image recognition dataset called MNIST and build a basic one-dimensional neural network, often called a multilayer perceptron, to classify our digits and categorize black and white images.