Intro to Deep Learning and Generative Models Course - L3.3 Vectorization in Python

Sebastian's books: https://sebastianraschka.com/books/ When we implement machine learning models, and especially deep learning models, it is very important to write efficient code in order to make training these models feasible. One aspect of writing efficient code is using vectorization, for example, replacing Python for-loops with more efficient linear algebra code such as dot products (via NumPy). In this video, we will see a few examples of vectorization in Python and NumPy. Slides: https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L03_perceptron_slides.pdf Code for the vectorization example: https://github.com/rasbt/stat453-deep-learning-ss21/blob/main/L03/code/vectorization-example.ipynb ------- This video is part of my Introduction of Deep Learning course. Next video: https://youtu.be/TlGpIKMVoOg The complete playlist: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51 A handy overview page with links to the materials: https://sebastianraschka.com/blog/2021/dl-course.html ------- If you want to be notified about future videos, please consider subscribing to my channel: https://youtube.com/c/SebastianRaschka