Google Xception was developed in 2017 by researchers at Google. It was created to improve image classification performance by using depthwise separable convolutions, which allowed for more efficient computation and higher accuracy compared to previous models.
Google Xception
Google Xception is a deep learning model designed for image classification tasks. It leverages depthwise separable convolutions to enhance computational efficiency while maintaining high accuracy in recognizing and categorizing images.
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About Google Xception
Strengths of Google Xception included high accuracy and computational efficiency due to depthwise separable convolutions. Weaknesses involved potential complexity in implementation and higher resource requirements for training. Competitors included models like ResNet, Inception, and VGG.
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How to hire a Google Xception expert
A Google Xception expert must have skills in deep learning, specifically with convolutional neural networks (CNNs), proficiency in Python programming, experience with TensorFlow or Keras frameworks, and knowledge of image processing and classification techniques.
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