Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.So what are those things?Numpy. This forms the basis for everything else.  The central object in Numpy is the Numpy array, on which you can do various operations.The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.That means you can do vector and matrix operations like addition, subtraction, and multiplication.The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.Pandas makes working with datasets a lot like R, if you’re familiar with