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DL Series: Convolutional Neural Networks (CNNs)
Part IV
*Before reading this article, make sure to check out the first three parts of our four-part series on deep learning:
- Part I: What is Deep Learning?
- Part II: Artificial Neural Networks (ANNs)
- Part III: Recurrent Neural Networks (RNNs)
What’s this about: In this final installment of our DL series, we will cover another one of the major deep learning networks: convolutional neural networks (CNNs).
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CNNs are crucial to computer vision and image recognition tasks. Prior to their development, these tasks required manual and time-consuming feature extraction methods to identify objects in images. When it comes to image recognition, a CNN’s role is to reduce images into an easily processable form while maintaining features that are crucial to good predictions.
How do CNNs Work?
CNNs are superior to other neural networks given their incredible performance with image, audio signal, or speech inputs.
They rely on three main types of layers to carry out their tasks:
- Convolution Layer: recognizes features in pixels.
- Pooling Layer: makes features more…
