What are Convolutional Neural Networks (ConvNets or CNNs)

Convolutional neural networks (ConvNets or CNNs) are more often utilized for classification and computer vision tasks. Convolutional neural networks are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They have three main types of layers, which are:

Convolutional layer Pooling layer Fully-connected (FC) layer The convolutional layer is the first layer of a convolutional network. While convolutional layers can be followed by additional convolutional layers or pooling layers, the fully-connected layer is the final layer. With each layer, the CNN increases in its complexity, identifying greater portions of the image. Earlier layers focus on simple features, such as colors and edges. As the image data progresses through the layers of the CNN, it starts to recognize larger elements or shapes of the object until it finally identifies the intended object.

• Inline expression: $f(x) = x + 1$

• New line + centered expression:

  $$f(x) = x + 1$$

  $\int_0^\infty = \frac{1}{x^2+1} \;dx=\frac{\pi}{2}$