Introduction to Deep Learning

Overview

Delve into the cutting-edge world of Deep Learning with our comprehensive course. This introductory program is designed for individuals eager to explore the depth of natural language processing, biomedical applications, and more.

Deep Learning empowers the handling of varied data types, including images, texts, voice, graphs, and others, equipping learners with the skills to build and train advanced neural networks such as multilayer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE), and generative adversarial networks (GANs).

Engage in hands-on projects that span across detecting cancer using CNNs, analyzing disaster tweets with RNNs, and creating dog images through GANs. This course necessitates prior coding or scripting knowledge, with a significant emphasis on Python.

While it's beneficial to have completed the preceding courses, 'Introduction to Machine Learning:

Supervised Learning' and 'Unsupervised Algorithms in Machine Learning', they are not mandatory. However, a college-level understanding of Calculus and Linear Algebra is essential, as some sections of the course will be math-intensive.

For those interested in furthering their education, this course also counts towards academic credit for CU Boulder’s MS in Data Science or MS in Computer Science degrees available on Coursera.

These degrees are tailored for both recent graduates and working professionals, featuring targeted coursework, short 8-week sessions, and flexible, pay-as-you-go tuition. Admissions focus on performance in three preliminary courses rather than academic history, perfect for those looking for a practical and accessible entry into higher education.

Discover more about the MS in Data Science:

https:

//www.coursera.org/degrees/master-of-science-data-science-boulder

Explore the MS in Computer Science:

https:

//coursera.org/degrees/ms-computer-science-boulder

This course is offered through Coursera by Wesleyan University and falls under several categories including Artificial Intelligence Courses, Deep Learning Courses, and Neural Networks Courses.

Course cover image credited to Ryan Wallace on Unsplash.

Taught by

Nikita Kazeev, Andrei Zimovnov, Alexander Panin, Evgeny Sokolov and Ekaterina Lobacheva

Subjects

russia