Sequence Models
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
Course Description
Instructor Details
- 4.9 Rating
755 Reviews
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.
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Reviews
By Jason J D on 11-Sep-19
Wonderful end to this Deep Learning Specialization. The programming assignments cover up a variety of hot topics in the Deep Learning market. The videos are very well made and teach the content in depth. A special thanks to Prof. Andrew for yet another amazing course in this wonderful specialization!
By Martin C S on 13-Jul-19
Assignments don't match the quality of the other four courses of this specialization. Automatic grading accepts solutions despite results not matching expected results. This should be fixed.
By Sundeep R on 12-Jul-19
Many topics are only quickly skimmed over. Serves as an overly brief introduction to RNN.
By Abhijeet M on 1-Jul-19
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
By Beibit on 26-Jun-19
Little bit math heavy. It was sometimes hard to understand the intuition, e.g. RNN, LSTM, GRU
By Zhongyi T on 11-Jun-19
Poor submission system. Failed many times to upload and had to redo the assignments. I was using a 250Mbps high speed network. Also course materials are problematic. The instructors are not willing to fix the problems for many years.
By Ozioma N on 9-Jun-19
Great module, I am lucky to have used this resources in learning sequence models, I can imagine running LSTM using one of the frameworks without ever implementing it myself, Andrew Ng/Deeplearning.ai is the best!
By Jinxiang R on 26-May-19
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
By Lewis C L on 15-Apr-19
Full of appalling errors that have been present for over 1 year. No one fixes it. It is clear that since Ng was let go by Stanford and Baidu, he is trying to earn a living with deep learning_ai. This apparently is not working as the small income from Coursera is not sufficient. As a result the prerecorded classes remain on Coursera to accrue some residual income. But, Andrew Ng and the staff are apparently gone.Sadly, since these classes are no longer based on REAL Stanford classes the quality has gone downhill. I would recommend not taking the deeplearng_ai classes. Stick to classes offered by currently employed professors at established universities--preferably classes that ARE the same as the university classes or, at least, those derived from actual classes.
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Overall Score : 98 / 100





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