DM
Learnt many things and most exciting was Python code part
In this course, you’ll be learning about Computer Vision as a field of study and research. First we’ll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. Then we’ll introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results and discuss advantages and drawbacks of both types of methods. We'll use tutorials to let you explore hands-on some of the modern machine learning tools and software libraries. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation.
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
DM
Learnt many things and most exciting was Python code part
CC
Great content and clear, succinct explanations of the concepts!
JP
Great introductory course on deep learning for computer vision.
HT
A well comprehensive course with good explainations for terminologies and concepts in DL
AY
Great Course, The instructor explained the mathematical aspects of the course in a clear manner.
AL
Very good introduction but the practical exercises are so easy.
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Great introductory course on deep learning for computer vision.
Very good introduction but the practical exercises are so easy.
Good theoretical lessons and material suggested for further readings. However from a practical point of view the course is pretty lazy, both in showcasing the implementations of the "pytorch" library and in the requests of the assignments, which consist in remembering what was shown during lectures and copying it.
This course provided valuable insights into deep learning and computer vision applications. The theoretical and practical lessons were very informative, and I gained the skills needed for my project. The hands-on examples and projects were particularly helpful. As a high school student, I feel that this course laid an important foundation for deep learning. I highly recommend it.
Es un curso muy bien explicado, abarcando los conceptos básicos sobre el tema de visión por computador. Fue muy enriquecedor para mi ya que cumplió con mis expectativas. me gustó mucho la parte final que se abordó de manera práctica.
Professor Fleming is explaining verry good. Even is most of the concepts were not new to me it was a plessure how it was explained.
Great Course, The instructor explained the mathematical aspects of the course in a clear manner.
A well comprehensive course with good explainations for terminologies and concepts in DL
Great content and clear, succinct explanations of the concepts!
Learnt many things and most exciting was Python code part
Un curso muy bueno
Amazing course
good. Thanks
thank you.
Great
Good
A pretty good course , The course is suitable for both beginners with no prior experience in computer vision and intermediate learners looking to enhance their knowledge and skills.
It is a nice introduction.
Great one for beginners!
Good