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Fundamentals for Artificial Intelligence and Deep Learning Framework
This class will introduce fundamentals of machine learning techniques and deep dive in cutting edge concepts that enabled neural networks to achieve state of the art performance in many visual, textual, and biomedical problems. Fundamental concepts like feed forward networks, convolution networks, recurrent neural networks, back propagation, loss functions, batch gradient descent, and stochastic optimization will be studied.
Provider Information
Heather Markle
Maryland Applied Graduate Engineering at the University of Maryland
2105 J.M. Patterson
College Park, MD 20742
Course Overview
Linear algebra | Fundamentals of programming | Fundamentals of statistics
4356 Stadium Drive
College Park, MD 20742
Learning Objectives
Describe the fundamental concepts in machine learning, and the reasons behind the rise of neural networks to scale with today’s big datasets. Formulate machine learning problems and identify suitable neural networks models to solve them. Use modern neural networks frameworks (e.g., Keras, TensorFlow, PyTorch) to train, validate, test, and debug state of the art models. Address challenges, identify solutions, and explore opportunities in using neural networks in various application domains.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.