👉 Differential computing is an advanced approach to machine learning that optimizes the training process by focusing on the gradients of the model's parameters, rather than just the overall loss function. This method leverages the concept of automatic differentiation to compute these gradients efficiently across all layers of a neural network, even in complex architectures. By doing so, it enables more precise and faster updates to the model's weights, leading to improved performance and reduced training time. Unlike traditional backpropagation, which can be computationally intensive, differential computing distributes the gradient calculations across multiple devices (like GPUs or TPUs), making it highly scalable and suitable for large-scale deep learning applications. This technique is particularly beneficial in scenarios where real-time performance and resource efficiency are critical, such as in autonomous driving or real-time speech recognition systems.
