Instead of repeating the same study patterns, focus on creating a more efficient schedule that prioritises building a strong ...

The course contains gradient and directional derivative, divergence and curl, circulation and flux, Gauss and Stokes theorems, computation of pressure force, particle derivative and acceleration, ...

We will finish by learning how deep learning libraries like Tensorflow create computation graphs for gradient computation. This week, you will have two short quizzes, a Jupyter lab programming ...

This page contains links to calculus tests offered at UAB in the past, according to the syllabus adopted at that time. Most tests are given without answers. The department does not keep answers to the ...

Additional explanation of computation graphs, memory usage, and gradient computation strategies, can be found in the blog post accompanying our package. import tensorflow as tf import ...

Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics ...

Machine Learning is about developing systems that automatically improve their performance through experience. It has found applications in many AI systems and products. Examples include systems that ...

The course introduces a variety of central algorithms and methods essential for studies of statistical data analysis and machine learning. The course is project-based and through the various projects, ...