The Deep Learning Fundamentals Lab is an advanced learning opportunity designed to help you master the core concepts behind deep learning through two units of six hands-on projects each - ranging from using PyTorch to build models to applying CNNs to real-world problems. Applicants are expected to have the following prerequisite skills:
Basic linear algebra (i.e., matrices, vectors, and matrix operations)
Basic calculus concepts (i.e., function analysis, derivatives, gradients, etc.)
Basic probability and statistics functions
Intermediate-level Python programming, including: basic data structures like arrays and dictionaries, the ability to write definitions for functions and classes, and familiarity with data manipulation using libraries like NumPy and Pandas.
Familiarity with essential machine learning concepts, including supervised and unsupervised learning, overfitting and regularization, and training, validation, and test sets
Before you attempt the Admissions Assessment, we recommend that you use the following free resources to help you prepare:
Mathematics for Machine Learning: Comprehensive coverage of linear algebra, calculus, and probability.
Algebra Basics at Khan Academy: Foundations, algebraic expressions, linear equations and inequalities, graphing lines and slope
Statistics and Probability at Khan Academy: Displaying and comparing quantitative data, summarizing quantitative data, modeling data distributions.
Python at LearnPython.org: Learn the Basics
Applied Data Science Lab: WQU’s own Applied Data Science Lab is free and always available. The Applied Data Science Lab teaches you the Python and Machine Learning skills needed to succeed in the Deep Learning Fundamentals Lab.