Jaishree https://github.com/JaishreeJanu

Maths and intuition behind gradient descent. Also explain difference between optimization algorithms and learning/training of a machine learning model.

Linear versus nonlinear phenomena When the change in the output of a system is directly proportional to the change in input, the system exhibits a linear phenomenon. All other phenomena are nonlinear. Linear phenomena are much simpler and coherent than nonlinear phenomena. Maxwell’s theory of Electromagnetism [1] is a linear phenomenon. Millions of electromagnetic signals travel in space among cell phones through satellites, but they don’t interfere with each other. The data and videos sent via the signals are neither lost nor created during the transmission.

Data Structures and Algorithms In order to solve a computer science problem, we need a set of instructions (an algorithm) that operate on certain input data and produce some output data. Data structures provide efficient storage and efficient access, and modification of data in a computer’s memory [1]. Data Structures and Algorithms are the key foundations required to solve any computer science problem efficiently. Whereas Machine Learning is a powerful medium to solve today’s problems using data, which under the hood is the consumer of data structures and algorithms.