Gradient Descent is a key algorithm in deep learning, serving as a central optimization technique to minimize the error between actual and predicted outputs in machine learning models. The process involves iterating through steps where random initial weights are adjusted based on calculated errors, using a method that identifies gradients to guide these adjustments. The article explains the intuition behind gradient descent by illustrating how a function's minimum point is approached through calculated jumps, using examples like f(x)=x^2. Implementations of gradient descent are demonstrated using numpy and PyTorch, showcasing various methods of calculating derivatives and updating weights, including the use of PyTorch's built-in stochastic gradient descent function. The algorithm's effectiveness is exemplified by a visualization of iterative adjustments leading to the function's minimum point, emphasizing its role in deep learning.