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Learner Reviews & Feedback for Introduction to Deep Learning by University of Colorado Boulder

3.6
stars
23 ratings

About the Course

Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. We recommend taking the two previous courses in the specialization, Introduction to Machine Learning: Supervised Learning and Unsupervised Algorithms in Machine Learning, but they are not required. College-level math skills, including Calculus and Linear Algebra, are needed. Some parts of the class will be relatively math intensive. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image by Ryan Wallace on Unsplash....
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1 - 6 of 6 Reviews for Introduction to Deep Learning

By Saksham

•

Nov 15, 2023

good

By Daniil H

•

Jun 7, 2022

#1

By Anubhav S

•

Aug 25, 2023

This is a course containing interesting topics but lack proper teachings for a student. Deep Learning Specialization takes 3 - 4 courses to teach its 4 weeks and its last week assignment requires us to do a Kaggle project on Cyclic GAN which course material or even readings didn't cover that specific topic.

Overall, 4-star rating precisely because it covers a lot of topics. Beyond it to actually learn it, if you are learning from scratch, go to other sources.

By Zehu C

•

May 17, 2022

Last course of the machine learning specialization. this is a comprehensive introduction to deep learning, that covers basic topics of DL. But the professor didn’t really do a good job of explaining concepts. The lecture doesn’t help with doing the assignment. I wound’t recommend anyone that is not from the MSDS program to take this course. There are better courses to take on Coursera.

By Rog

•

May 9, 2023

Absolutely terrible like all other courses in this particular ML series. The instructor spends most of the time solving math in an impossible to understand manner and does not explain any of fundamental concepts properly. Had to resort to external resources to be able to understand anything.

By Tufail M

•

Feb 28, 2024

This is the first time I feel let down by the instructor and the course materials. The content covered in lectures and the quizzes seems disconnected and quite unusual, not aligning well with the lecture content.