What are some common visualization techniques used in deep learning, and why are they important?

Common visualization techniques in deep learning include activation maximization, saliency maps, and t-SNE plots. These methods help interpret model behavior and performance. Activation maximization shows what input patterns produce high activations in a neuron, useful for understanding feature representations. Saliency maps visualize gradients of the output concerning input pixels to highlight important areas for classification. t-SNE plots help visualize high-dimensional embeddings in lower dimensions, revealing clusters or relationships between data points, which is essential for understanding how models learn and identify patterns.