Machine Learning, Data Science, Artificial Intelligence.
small changes in the output around 𝑓(𝑥₀). Continuity in deep learning is crucial for ensuring smooth changes in model outputs as inputs vary, which helps in stable training. It allows for the use of gradient-based optimization techniques, like backpropagation, essential for training neural networks