Convolutional Neural Networks

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language

Created by: Andrew Ng

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Overall Score : 100 / 100

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Course Description

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.

Instructor Details

Andrew Ng

Andrew Ng is Co-founder of Coursera, an and Adjunct Professor of Computer Science at Stanford University. His machine learning course is the MOOC that had led to the founding of Coursera! In 2011, he led the development of Stanford University's main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class to over 100,000 students, thus helping launch the MOOC movement and also leading to the founding of Coursera.Ng also works on machine learning, with an emphasis on deep learning. He had founded and led the "Google Brain" project, which developed massive-scale deep learning algorithms. This resulted in the famous "Google cat" result, in which a massive neural network with 1 billion parameters learned from unlabeled YouTube videos to detect cats. Until recently, he led Baidu's ~1300 person AI Group, which developed technologies in deep learning, speech, computer vision, NLP, and other areas.



110 total reviews

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By Gyuho S on 25-Apr-19

This course is definitely tougher than the first three courses. Challenging but worth it.

By Farzeen H on 12-Jan-19

Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.

By Aleksa G on 13-Jan-19

Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.

By David B C S on 17-Dec-18

Great course, easy to understand and very useful. The explanations are very clear, as is expected from the professor. The purpose of the course is for you to have a practical comprehension of CNNs, it will give you the necessary tools to implement you own networks, but it will not get into the specifics of each model. Nevertheless, all of the resources are referenced, which makes it very easy for you to dig deeper on any specific topic covered on the course.

By Ed B on 3-Nov-17

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

By Joshua M on 31-Jul-19

Content is great, but videos could be trimmed to cut retakes. A big issue is that guidance for programming assignments abruptly drops off from extreme hand-holding to being thrown in the deep end.

By Huijun P on 18-Apr-19

Great lectures but the programming assignments feel as if it is testing your proficiency with tensorflow which is neither formally covered in the lecture nor the most intuitive framework to understand so you'll spend so much time digging through convoluted tensorflow documents and qna and whatnot to debug your codes that you would rather learn tensorflow formally first and then take this course and still end up finishing it faster than only going through this course only but it is only the programming assignments that basically assume that you are already familiar with the tensorflow framework so if you are only going to go over the video lectures it gives a great overview of how CNN works and many useful algorithms which can applied to a assortment of situations

By Xinwei B on 13-Feb-19

When I am doing the programming assignments, I felt that some part were quite difficult since I had no background in neither Keras nor Tensorflow. It was helpful that in one of the previous courses there was a tutorial for the basics of Tensorflow. But for Keras I felt that there is a gap between what I have and what is needed for the assignment. So I would suggest a more thorough tutorial for Keras. Maybe several short tutorials talking about the implementations and ideas of Tensorflow & Keras may help a lot.

By Stefan J on 30-Dec-18

Theoretical material was great as always. However, programming assignments were poorly commented in some cases which results in unnecessary confusion.

By fabrizio f on 17-Dec-18

Very good however most of the effort is applied in learning and applying programming (tf, Keras) than actually thinking about the DL models and practicing different scenarios.

By Anne R on 9-Oct-19

Out of the four courses I have taken in the deepai sequence this is the best one! This course got to the heart of the methods that researchers are implementing and also dropped you into the programming using Tensorflow. As noted by some other reviewers there are places where more instruction could be helpful, but I felt that this course obtained a good balance between information and challenging and also between concepts and hand-on implementation. A couple of the prior courses could be merged and then this would be the 2nd or 3rd course in the sequence which would be much better in getting students to complete the projects and all courses.

By Markus B on 5-Dec-18

Great course. The only improvement I'd wish is to get a better introduction to the concepts of Tensorflow and Keras.