This course provides an in-depth introduction to Deep Learning, a powerful technique widely used in various applications such as natural language processing and biomedical fields. It covers the fundamentals of Deep Learning (DL), including building and training multilayer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE), and generative adversarial networks (GANs).
Several hands-on projects are included, focusing on tasks like cancer detection using CNNs, analyzing disaster tweets with RNNs, and generating images of dogs using GANs. You will need prior coding or scripting knowledge, focusing on Python programming. College-level math skills, including Calculus and Linear Algebra, are necessary, as some parts of the course involve mathematical concepts.
🆓 Free to Audit
🕒 Approx. 60 Hours
📈 Intermediate Level
🧾 Paid Certificate Available Upon Completion
🎓 Offered by The University of Colorado Boulder via Coursera
