Sequences, Time Series and Prediction

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.In this four-course Specialization, you'll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network's performance using convolutions as you train it to identify real-world images. You'll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input

Created by: Laurence Moroney

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Overall Score : 86 / 100

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Course Description

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.In this fourth course, you will learn how to build time series models in TensorFlow. You'll first implement best practices to prepare time series data. You'll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you'll apply everything you've learned throughout the Specialization to build a sunspot prediction model using real-world data!The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

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Instructor Details

Laurence Moroney

Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. As the author of more programming books than he can count, he's excited to be working with deeplearn.ai and Coursera in producing video training. When not working with technology, he's a member of the Science Fiction Writers of America, having authored several science fiction novels, a produced screenplay and comic books, including the prequel to the movie 'Equilibrium' starring Christian Bale. Laurence is based in Washington State, where he drinks way too much coffee.

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Reviews

4.3

111 total reviews

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By Asad K on 31-Jul-19

The first week has some interesting discussion of time series data and some traditional non-ML methods for forecasting, but beyond that the course quickly divulges the all too familiar weaknesses of this specialization; lack of depth, elementary discussion, weak insight into common problems that arise during training models, and extremely poorly written quizzes that don't test the learner's gain of knowledge or skills in any meaningful way.My biggest complaint to the instructors and the team is that for months this specialization promised the last course will discuss the WaveNet model, but the course didn't even do a cursory survey of it (In week 4, the instructor adds a Conv1D layer but doesn't even discuss the causal padding and completely skips dilations, etc, so that in effect there isn't even discussion of a single layer from WaveNet model). Sigh !

By Fengjun W on 18-Aug-19

Finally, wasted my weekend and 40 euros to finish this shitty specialization. I really dont know the target audience of this specialization. If you have no background of deep learning, going through some code snippets without any explanation wont help you at all. you can't know anything behind it. If you already have some knowledge, you will find nothing new and more in this course. 1) The materials are so shallow and without any depth, just reading the slides and codes with errors. Only some high-level keras APIs are covered. The official tensorflow tutorial is much better. 2) The test questions are of no value at all, it cant test any your understanding whether about deep learning or the tool tensorflow. The assignments are poorly designed, the answers contains errors. 3) I strongly doubt the instructor, I think he does not have much ML experience. Please don't waste your money and time on this specialization. If you want to learn deep learning, go to cs230; cs231n for computer vision; cs224n and cs224u for NLP; cs20 for Tensorflow.

By Irina G on 2-Aug-19

Very weak course, shallow, lacks content. Can be "learned" in a few hours, not weeks. Really hoped to see a working ML model for a time sequence, but the examples shown in this course do not demonstrate why bother with ML. If these examples were middle-school home work, they would be graded D+(keep trying or better use other methods). The instructor doesn't come across as an experienced ML practitioner.

By Steve H on 7-Aug-19

Very superficial presentation of the material, and disappointing content given all the initial hype. Whatever happened to working with WaveNet? The 4 weeks to complete the course is a massive over-estimate. Expect to spend not more than a day going through the course. Quiz questions are very low value and do not test any understanding.

By Subhadeep D on 31-Jul-19

Quite a good light-weighted course on Time Series and Prediction. It was quite helpful for people like me who are seeking ways to implement the concepts.

By Marghoob K on 3-Aug-19

This was really a beautifully designed course. They didn't focused on teaching too much of thing at once but build up the base slowly and strongly for better understanding.

By Parab N S on 14-Sep-19

An excellent course on Time Series and Sequences by Laurence Moroney. Explained how to use CNN, RNN and DNN together to bring the nest out of time series prediction.

By Silviu M on 2-Sep-19

The material is great and the presentation elevated and professional. Few thoughts nonetheless: a) i know time series, came here for specific advice on how to tune models. I was extremely disappointed. Stationarity is mentioned at the very beginning but then it fades as if it was completely irrelevant to ML. b) there is more than one contradiction in the presentation. MAE is going up yet the presenter says that it got better??? That I think would be really confusing, particularly for novice learners. c) black boxes: I acknowledge that there are so many decisions and choices one needs to make when setting up a training model. Wouldn't it be relevant to highlight them and explain how different decisions impact the outcome? This course was failing on that.

By Kaan A on 24-Aug-19

Unfortunately, These whole Specialization didnt match my expectations. I finished whole Deep Learning Specialization and I LOVED IT. Before starting this one I had very good feeling about this specialization; however I learned very little. Most of the videos are like "this code does this and this code does this and this line does this and this function does this etc. " . A bit disappointed, but still learned some.

By Sergei A on 31-Jul-19

Thank you guys for this course!

By Stewart A on 4-Aug-19

Really cool course. Great material. Great presenter. Enjoy!

By Christopher G on 2-Aug-19

I quickly learned a lot about how to represent time series and sequence data for prediction tasks, and how to combine different deep learning techniques together to get high-quality predictions. Another great course!