Скачать с ютуб Affine Transformations — Topic 27 of Machine Learning Foundations в хорошем качестве

Affine Transformations — Topic 27 of Machine Learning Foundations

In this video we use hands-on code demos in NumPy to carry out affine transformations, a particular type of matrix transformation that may adjust angles or distances between vectors, but preserves parallelism. These operations can transform the target tensor in a variety of ways including scaling, shearing, or rotation. Affine transformations are also key to appreciating eigenvectors and eigenvalues, the focus of the next videos in the series. There are eight subjects covered comprehensively in the ML Foundations series and this video is from the second subject, "Linear Algebra II: Matrix Operations". More detail about the series and all of the associated open-source code is available at github.com/jonkrohn/ML-foundations The next video in the series is:    • Eigenvectors and Eigenvalues — Topic ...   The next video in the series will be published shortly and the playlist for the entire series is here:    • Linear Algebra for Machine Learning   This course is a distillation of my decade-long experience working as a machine learning and deep learning scientist, including lecturing at New York University and Columbia University, and offering my deep learning curriculum at the New York City Data Science Academy. Information about my other courses and content is at jonkrohn.com Dr. Jon Krohn is Chief Data Scientist at untapt, and the #1 Bestselling author of Deep Learning Illustrated, an interactive introduction to artificial neural networks. To keep up with the latest from Jon, sign up for his newsletter at jonkrohn.com, follow him on Twitter @JonKrohnLearns, and on LinkedIn at linkedin.com/in/jonkrohn