Kernels vs. Filters: Demystified

For most of us, who were once newbies in Deep Learning, trying tf.keras.layers.Conv2D for MNIST classification was fun. Convolutions are the building blocks of most algorithms in computer vision, except for some newer variants like Vision Transformers, Mixers etc. which claim to solve image-related problems without the use of convolutions. At the core of DL, lies Gradient Descent ( and its variants), which help us optimize the parameters of a NN, which in turn reduces the loss we incur while training the model. Convolutions or Convolutional layers also possess their own parameters commonly known as filters. No, not filters but they are kernels, right?