Gradient Descent (Part 1)

Gradient descent is an algorithm for finding the local minimum of a function. Many problems in machine learning, mathematical modeling, physics, etc. involve optimization-- finding the minimum or maximum-- of a function. Gradient descent, or variations of it , is a common algorithm used for optimization in all the fields noted above. We’ll go over the basics of gradient descent and implement a simple version in Python. In parts one and two, we will restrict ourselves to functions of only one variable, but in parts three and perhaps four, we will look at functions of multiple variables that more closely resemble real-world applications.

Part 2: https://youtu.be/J1ghebX8XGY
Part 3: https://youtu.be/Twxe59IjHDk

Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/gradient_descent

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