Chevron Left
Sequences, Time Series and Prediction(으)로 돌아가기

deeplearning.ai의 Sequences, Time Series and Prediction 학습자 리뷰 및 피드백

4.6
655개의 평가
105개의 리뷰

강좌 소개

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....

최상위 리뷰

OR

Aug 04, 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.

MK

Aug 03, 2019

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.

필터링 기준:

Sequences, Time Series and Prediction의 105개 리뷰 중 51~75

교육 기관: divya p p

Sep 16, 2019

excellent explanation and concepts on using tensorflow in real life datasets. very informative jupyter notebooks.

교육 기관: Richard S

Sep 15, 2019

This course was my ultimate motivator and goal for taking the specialization as I am doing work with time series. Very interesting to learn a traditional statistical approach, then apply DNNs, RNNs, LSTMs and CNNs to time series prediction. Even though just scratching the surface, I can apply knowledge from this course and specialization immediately.

Thank you Laurence and Andrew for a fantastic course and specialization! I am inspired and motivated to dig deeper into the theory of NNs and their application with further courses and projects.

교육 기관: Parab N S

Sep 14, 2019

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.

교육 기관: 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.

교육 기관: Karunanidhi M

Sep 19, 2019

Helped me solve a real-time Prediction Challenge Excellent Course.

교육 기관: Pachi C

Sep 20, 2019

Fantastic course

교육 기관: 李英斌

Sep 18, 2019

nice!

교육 기관: Mahalingam.P.R

Sep 18, 2019

Perfect. Bring more like this...

교육 기관: Andrei N

Sep 21, 2019

Very detailed step by step tutorials of using Tensorflow with lots of effort to make things as easy to understand as possible. Especially, examples of generation a time-series pattern simulations looks very thoughtful and helpful for the course topic. A little lack of theory comparing to other courses by deeplearning.ai. Quizzes are quite undeveloped. But that is understandable, because the main goal of the course to introduce Tensoflow.

교육 기관: Ed M

Sep 23, 2019

This is easily the best material I've seen on time series analytics using DL. Lawrence Moroney does an unbelievable job of demonstrating how to create a practical time series model using different architectures from start to finish in a completely understandable way. Thank you so much!

교육 기관: Rodrigo R N

Sep 24, 2019

Show!

교육 기관: Marco A P N

Sep 24, 2019

It's very great

교육 기관: Yaron K

Sep 30, 2019

A step by step explanation of how to use TensorFlow 2.0 for building a Neural network for sequences and time series. With detailed examples of code and of how to choose hyper-parameters.

교육 기관: 全玉湖

Sep 30, 2019

Great courses i have learned, thanks a lot!

교육 기관: Nitish K T

Oct 03, 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.

교육 기관: Ahmed O

Oct 03, 2019

great course, It really simplifies a lot of important concepts

교육 기관: PRANAV S D

Oct 03, 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.

교육 기관: Yergali B

Oct 06, 2019

Finally! We did!

교육 기관: Carlos V

Oct 06, 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.

교육 기관: Ravi

Oct 11, 2019

Great course.

교육 기관: Rahul R

Oct 09, 2019

Wonderful course........these courses tough me that there is always room for improvement. Just we need to try with small steps in the right direction. DL is not just achieved using algorithms but need patience and trial and error too. Thanks to Laurence Moroney and Andrew Ng for this wonderful course.

교육 기관: Matteo

Oct 11, 2019

Interesting and usefull

교육 기관: Nhan T N

Oct 14, 2019

Nice course, I achieved more from this course about sequence model. Thank so much!

교육 기관: Akhil K P

Oct 11, 2019

It is a very good ready to go and really a practical example driven Course.

교육 기관: Roozbeh G

Oct 17, 2019

Short, effective, and practical. Some of the best material in the field.