Broadcasting in Machine Learning is a powerful technique that allows data scientists to efficiently perform certain operations on arrays of different shapes. It is a technique that is used in many areas of machine learning, such as neural networks and deep learning.

Broadcasting is a useful way to perform mathematical operations on arrays of different shapes. It enables us to perform operations on arrays of different sizes without needing to explicitly create a new array of the same size. This is especially useful when working with large datasets.

Broadcasting works by “stretching” the smaller array to match the shape of the larger array. This is done by creating new elements in the smaller array and filling them with a constant value. This constant value is then used to perform the operations on the larger array.

For example, suppose we have two arrays, A and B. Array A is of size 3×3 and array B is of size 1×3. We can use broadcasting to perform an operation on these two arrays. The operation would stretch the smaller array (B) to match the shape of the larger array (A). This is done by filling the missing elements in B with the constant value 0. Now, the operation can be performed on both arrays.

Broadcasting is a powerful technique that can dramatically reduce the amount of code needed to perform certain operations on arrays of different shapes. It is used extensively in machine learning and deep learning applications. By taking advantage of broadcasting, data scientists can quickly and easily perform complex operations on large datasets.