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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....

JH

Mar 21, 2020

Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.

OR

Aug 3, 2019

It was an amazing experience to learn from such great experts in the field and get a complete understanding of all the concepts involved and also get thorough understanding of the programming skills.

筛选依据：

创建者 samina y

•Mar 27, 2022

It was easy to follow up all the lesson because of the way the course has been structured. I even tried in other places to learn tensorflow but i was not able to continue after few days. but this structured course was the best. I am confident about the knowledge i got.

创建者 Rodrigo R C P

•Jun 13, 2020

I think the goal of the course was met and the practice and examples provided are very useful. Although, it would have been nice to learn about cross validation methods for time series in order to compare and select the best model architecture for our real world data.

创建者 Mario T

•Sep 19, 2019

Congratulations to Lawrence, great course. I did the other way: I fist went through Andrew's courses and then Lawrence's. I believe is best to have the theory on how DNN works, and then to to practice.

Thank you! I look forward for the new courses you will be offering.

创建者 Aroua G

•Dec 28, 2019

Lots of good techniques and helper functions to work with time series. I wish there was more variety in the problems encountered (rather than prediction some time series classification for instance) but that doesn't affect the good quality of learning provided.

创建者 Richard D

•Aug 31, 2021

Really good course which gets you up to speed of time series prediction. It was fantastic for me as I have knowledge in most of deep learning but not recurrent neural nets. I picked up what was happening fast and it only took me a day to complete the course

创建者 Bilal B

•Feb 4, 2022

It is really good, every mentioned aspect explained in detail. There are 2 Jupyter Notebook for each week which supports the implementation skills. Also whole process was very smooth, I realize that I completed course when I see congratulations pop up.

创建者 Tibor K

•Sep 11, 2020

It was really refreshing to watch the recommended methods, but I think I have found a much better method for time series forecasting. In the next days I will compare what I have learned here with what I did by myself using Koopman operator based methods.

创建者 PRANAV S D

•Oct 3, 2019

The concepts from Deep Learning specialization by Prof. Andrew has explained well here for Keras. I expected graded programming assignments from scratch. Similar material you can find on internet but here it is very well compiled, organized, authentic.

创建者 Petyo P

•Apr 17, 2020

LOVE IT!

This course was a great investent of time and money. It was time efficient and gave a lot of information. I am fully motivated now to go into the Deep Learnign Specialization and to participate in futura specializations from these instructors.

创建者 Rehan U

•May 12, 2020

It's been a spectacular specialization with lots of hands-on practice. This Specialization has been one-stop for all the things to learn from TensorFlow.

Thanks to Laurence and Andrew🤗.

Looking forward for learn more exciting things from you✌✌

创建者 Ibrahim O

•Sep 3, 2019

It has been a great time putting time into this specialization there is still more to learn which I know you guys will always try your best to release more courses that will comprehend what we've learned in this specialization.

Thank you all.

创建者 Louis C

•Aug 13, 2021

Very clear explanations, no useless content, esay to use notebooks and a focus on practical implementation and use which is appreciated.

What's more, links to more theoretical content is provided, which gives a nice complement to the course.

创建者 Alfredo C

•May 9, 2021

Explanations are quite clear, and the examples are useful to understand how to apply different concepts. This course is helpful, after taking courses on deep learning and being familiar with concepts like LSTM, CNN, dense layers, and so on.

创建者 MD. A K A

•May 5, 2020

This course helped me to learn Time Series in the same way a machine learns from data. Without having much theoretical knowledge, now I can run basic models. Thanks a lot to deeplearning.ai team for proving such a practical oriented course.

创建者 Tharindu B A

•Aug 15, 2019

This course is amazing and above my expectations! Very good exercises, good speed, well communicated. The instructor made me feel very comfortable and was able to take many things away. Excellent content and very knowledgeable instructor!

创建者 Carlos V

•Oct 6, 2019

Fantastic explanations and techniques to use CNN and RNN to predict time series, I learned quite a lot by taking this course thanks very much to everyone at Coursera, Google and DeepLearning.AI that contributed to this excellent content.

创建者 Yanyun H

•May 13, 2020

Awesome course and amazing introduction to the time series predication using machine learning. Look forward to future more courses into more complex data scenarios. One of the best instructors I've had on Coursera. Thank you, Laurence.

创建者 Priyank S

•Nov 9, 2020

This course provided an idea about the time series analysis with DNN's, LSTM's and CNN's which is a full package in learning neural networks. Instructors were very good and explained each and every code very thoroughly. Thank you !!

创建者 Nitish T

•Oct 3, 2019

The best introductory course on Sequence Models. I was intimidated by this topic before taking this course. Now, after training LSTMs and RNNs by hand, I am more comfortable. Thanks to Lawrence and Andrew for such a great course.

创建者 Ali A B

•Jun 24, 2022

It is amazing! If you want to know how sequnces and time sereis are implemented in tensorflow it is so vital for you to take the course. The course started with a simple example and then the example developed through the course.

创建者 Ourania S

•Jan 16, 2021

It is really excited this course with so many nice different topics! Thanks a lot Laurence and Andrew for all your effort you have put on this course and moreover many thanks for transferring your knowledge in such an enjoy way!

创建者 Pavan Y

•Jun 30, 2020

after taking Andrew's Deep Learning, this course is a logical step in two sense: 1. you can practice more 2. you do not need to write everything that are already output there through packages like TensorFlow. Excellent course!

创建者 Zeeshan A

•Jun 25, 2020

The specialization covers brief introduction to the concepts of Computer Vision and Natural Language Processing. It introduces to TensorFlow and gives a hands-on practical experience over the tool through simple assignments.

创建者 Gaurav P

•Jun 22, 2020

One of the best Specialization Courses In Coursera, Thank You so much Laurence for creating such a wonderful course and being such a awesome mentor and Thanks to Andrew too!! :) Looking forward to more such amazing courses.

创建者 Daniel E

•Aug 7, 2019

The specialization was packed with best practice fundamentals and I appreciated the explanations and lab work. My only concern is the lack of business focused problem sets that might bring us more up to speed with industry.