What are convnets in neural network
This recipe explains what are convnets in neural network
Recipe Objective - What are convnets?
A ConvNet or Convolutional Neural Network (CNN) is the deep learning algorithm which takes image as an input, assign importance (learnable weights & biases) to various objects in the image and be able to differentiate one from other. The pre-processing required in the ConvNet is much lower as compared to other classification algorithms. ConvNets have the ability to learn the filters or characteristics while in the primitive method filters are engineered with hands and with enough training. ConvNet is a stack of layer_conv_2d and layer_max_pooling_2d layers. A ConvNet takes input tensors of shape (image_height, image_width, image_channels) and does not include batch dimension.
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Explanation of convnet.
Convolution layers learn local patterns that is in the case of images, patterns found in small 2D windows of the inputs as compared to dense layers learn the global patterns in their input feature space that is pixels in image data.
A convolutional neural network can learn spatial hierarchies of the patterns. The first convolution layer will learn small local patterns such as edges. The second convolution layer will learn larger patterns made of features of the first layers, and so on. This allows convnets to efficiently learn the increasingly complex and abstract visual concepts as the visual world is the fundamentally spatially hierarchical.
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