Table of Contents
How long does it take to complete deep learning coursera?
So in total, this specialization requires approximately 3 months with 75 hours of materials to complete, and I finished it in 3 weeks and spent an additional 1 week to review the whole courses.
Is deep learning specialization good?
Overall, I think the specialisation is a great resource to learn about Deep Learning and artificial neural networks. It contains a lot of material, and the quizzes and notebooks really test your understanding of the contents.
Why does deep learning work well in computer vision?
Deep learning in computer vision was made possible through the abundance of image data in the modern world plus a reduction in the cost of the computing power needed to process it.
Should I be disappointed by the deep learning course?
If you’re someone who has a bit more time to invest in the theoretical foundations of deep learning and wants to be an effective DEEP LEARNER after being done with the course, then… you may be disappointed. Remember what I said about the assignments being strongly bootstrapped?
What should I expect from the first course of the specialization?
The first course of the specialization offers a basic, surface-level understanding of how neural networks work, along with How and Why We Make Them Deep. Andrew will talk a little bit about Why You Should Deep Learn, though presumably you already want to because you’re taking the course.
Should you pay $49/mo for a deep learning course?
If you’re someone who wants to break into deep learning and get a job in AI somewhere fancy, I suppose $49/mo is a relatively cheap way to get something on your resume that suggests you know things about deep learning. At the end of the course, you get a certificate that you can put on your LinkedIn profile.
What are the best courses to take for a computer vision degree?
The fourth and fifth courses are the coolest: they teach you about Convolutional Neural Networks and Recurrent Neural Networks and walk you through cutting edge architectures for things like computer vision, face recognition, image captioning, natural language processing, music generation, neural style transfer etc.