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Convolutional Neural Networks

This course is a part of Deep Learning, a 5-course Specialization series from Coursera.

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.
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Rating 4.5 based on 1,495 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Feb 18 (9 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

deep learning in 69 reviews

I really can't recommend this course highly enough (and the same goes for the entire Deep Learning Specialization).

This course ignited me on understanding of CNN and other start-of-art CNN based deep learning networks.

Its a really start to become a practitioner in Deep Learning and keep focus on architectures and experiments.

Again, additional material on the derivation of gradient descent for filters could be provided) but it is deep learning, so it is expected.

It is definitely a must for people who are new in this field and are interested in Deep Learning field.

This is the 4th course in the series of deep learning that I finished.

Really recommend it for those who wants to explore the world of deep learning.

Great cours Excellent!one star missing due to grader problem A great course for deep learning freshmen !

computer vision in 54 reviews

You're not just learning about cutting edge computer vision techniques, carefully and thoroughly explained, you're gleaning the distilled wisdom of a true master of deep learning.

I recommend it to everyone seriously interested in Computer Vision advanced tasks.

From basics to advanced computer vision using deep learning is such a 'deep' learning :-) Excellent, I learned a lot, thank you so much!

It was my first exposure to computer vision/CNNs, and I can say that the introduction here is absolutely the best.

However, It would be great if the course introduce how to annotate images and read annotated images to data set in order to get start computer vision project from scratch for audiences who has zero experiences Enjoyed the course but the programming assignments weren't well designed I think.

Since I have a computer vision background I was expecting much more challenges at this points when doing the pratical assignments.

Course provides good overview over state-of-the-art techniques in computer vision.

Gave me a really close feel of deep learning for computer vision.

convolutional neural networks in 36 reviews

This was a very decent exploration of how Convolutional Neural Networks are used to solve various computer vision problems.

It is a great course that covers most part of Convolutional Neural Networks.

A great introduction to convolutional Neural Networks and applications with Tensorflow This course has been an eye opener to the power of DL technology.

excellent!This course gives me great help!Thanks better than many costly courses in other platforms.. Well thought out course on Convolutional Neural Networks Best course CNN is a tough topic to fully demonstrate.

great 非常好,学到很多,谢谢老师精心设计的课程 The lectures were excellent and helped me understand the key elements of convolutional neural networks.

This course covers the basics of convolutional neural networks , resnets, inception nets, yolo, style transfer, face recognition.The programming assignments mostly for yolo and face recognition is done with transfer learning , i think its only fair as they are computationally expensive to train.I am confident about all the materials covered in this course Andrew Ng as always breaks down the problem to the basics so you can understand them.Its a great course if you want to know and implement the well known computer vision problems with the well known algorithms.

A very good introduction to convolutional neural networks.

I believe it is useful to take this course in order and it makes sense to study it as a part of the series, though technically that is not necessary.This is one of the best courses to take if you want to understand the basics of Convolutional Neural Networks.

tensorflow and keras in 20 reviews

it contains tensorflow and keras hands-on examples The absolute best of course of between the 5 in this sequence!

The one complaint I have is that I wish the course wouldn't assume so much familiarity with Tensorflow and Keras frameworks in the assignments.

More can be done to explain the Tensorflow and Keras code.

A little more details about tensorflow and keras implementation of the algorithms could make it more helpful.

Tensorflow and Keras tutorials need improvement and further explanations.

A good balance between theory and practice, although the complete lack of TensorFlow and Keras fundamentals can be a bit frustrating.

You got the chance to practice Tensorflow and Keras framework in practice.

With the help of the well-designed and challenging programming assignments you can practice and reinforce what you have just learned by doing it yourself, while becoming familiar with popular NN frameworks such as TensorFlow and Keras.

looking forward in 20 reviews

The most exciting of the four courses so far, now looking forward to course 5. amazing projects and andrew is as fantastic as ever Really nice well explained course.

Looking forward o sequence models!

Looking forward to the final course!

Looking forward to recurrent NN course.

Looking forward to try my own models now.

Excellent material and presentations!The assignments are more challenging this time, so don't expect passing them too easily.Looking forward to the next course in the series!

Looking forward to completion of the specialization.

very frustrated Very interesting to learn but won't be useful for my role.Looking forward to sequence models!

neural style transfer in 19 reviews

A nice course on convolutional neural networks, face recognition and neural style transfer.

Great diving into the cutting edge computer vision algorithms (such as YOLO), the state of the art CNN architectures(ResNet, VGG, Inception Network, Siamese Network), with a variety of applications of this architectures and algorithms, such as self-driving system, neural style transfer generator and face recognition and verification!

I have a solid understanding of CNNs now, and I thought the neural style transfer and face recognition topics were super cool.

CNN is very powerful and it's the core of object detection, face recognition and neural style transfer.

Plus, you learn how to implement Neural Style Transfer (DeepDream) here!

-- Another example, on the programming assignment, on Neural Style transfer, it is POORLY explained how the framework works when it comes to setting a_G and a_C.

The intuition behin object detection/face recognition and neural style transfer are well explained, but some more details for understaing how these models work is missing in my opinion.

Having the grading system a bit more flexible would save everybody's time.I didn't understand all the operations we did in tensorflow in the neural style transfer programming assignment.


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Rating 4.5 based on 1,495 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Feb 18 (9 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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