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Отзывы учащихся о курсе Sequences, Time Series and Prediction от партнера deeplearning.ai

4.7
звезд
Оценки: 4,204
Рецензии: 676

О курсе

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....

Лучшие рецензии

MI
6 июня 2020 г.

I really enjoyed this course, especially because it combines all different components (DNN, CONV-NET, and RNN) together in one application. I look forward to taking more courses from deeplearning.ai.

OR
3 авг. 2019 г.

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.

Фильтр по:

126–150 из 676 отзывов о курсе Sequences, Time Series and Prediction

автор: bryan m

11 апр. 2020 г.

It really help to start using tensorflow in the time series prediction data, i was hopping at least one example of multivariate data, but other than that it was really helpful

автор: Erdem Ç

8 июля 2020 г.

Precise and to the point introduction of topics and a really nice head start into practical aspects of Time Series and Sequences and using the amazing tensorflow framework.

автор: Colman S

1 дек. 2019 г.

Great introduction to TensorFlow. Very broad coverage of the tools available in TensorFlow for Machine learning. Everything you need to get started on your first project!

автор: DaesooLee

9 мая 2020 г.

Short, concise, and practical. But maybe, it'd be better if some more practical examples are addressed that clearly shows the power of the (CNN-)LSTM model over DNN.

автор: Marco D V

25 нояб. 2020 г.

Great courses! Please do not evaluate this course as it is ! You need to take also the ML and Deep Learning previous courses before find the real taste of this one!

автор: Anjana K V

26 нояб. 2019 г.

Thank you Laurence sir and Andrew sir for putting together this course. It gives a good foundation to learn more about deep learning and its numerous applications.

автор: Gurpreet S

5 авг. 2019 г.

Fantastic course, starting from basic fundamentals of statistical forecasting to using Convolutional neural networks. I will use my learnings directly to my job.

автор: santiago r z

18 нояб. 2020 г.

Great courses made by great people. Every course shows how to nicely handle practical problems that need to be solved when implementing Deep Learning algorithm.

автор: Shreyansh G

21 февр. 2021 г.

There should be more explanation on why the convolution layer was used in the time series predictions and an explanation of time series data generation code.

автор: Olena I

25 окт. 2020 г.

Quite interesting and useful course. I wish there is more theory on LSTM embedded INTO the course, and not given as a reference to another course. Thank you!

автор: SMRUTI R D

22 июля 2020 г.

The practice problems could have been a bit more rigorous. you may think of prediction of stock prices as an exercise. Thanks a lot for this specialization..

автор: Lari B

30 авг. 2021 г.

Very didactic teacher, very good examples for those with basic and intermediate knowledge. I really liked it. Congratulations on the excellent course!

автор: Mike B

20 июня 2020 г.

Excellent coverage of both the breadth of tooling now available in TF2.x and methods for shaping time series data so you can easily apply deep models.

автор: Roxanne E B

14 окт. 2020 г.

That was a great Intro course for time series prediction! It was so much fun to watch the videos and the notebooks were very very helpful! Thank you!

автор: Mauricio G

7 февр. 2020 г.

Es un poco abstracto este curso, recomiendo haber tomado el curso de Sequence Models de Andrew para poder interpretar mejor la información del curso

автор: Ayuni

13 июня 2020 г.

this course is strictured, clear explanations with brief videos. this is a great course to learn about machine learning especially in Tensorflow

автор: Patrick L

29 апр. 2020 г.

It's quite useful. But it'd be great if some details could be more explicitly mentioned like the tutorial in Tensorflow. It'd be a lot clearer.

автор: Alex F

30 янв. 2020 г.

A challenging journey to learn Sequences and Time Series to deal with the real-world, which is of much fun in the course. Let's explore together.

автор: Lokesh B

1 мая 2020 г.

Very insightful. Although I think, some of the quiz questions can be improved. Those questions were less related to TensorFlow or deep learning.

автор: Tiffany K

14 мая 2020 г.

great course! It was very helpful for implementing time series prediction strategies, and I was able to implement things I learned right away.

автор: Rafael T

5 апр. 2020 г.

The course is great, I think it is much intended for beginners in Deep learning, specially those looking for the coding and practical aspects.

автор: Carlos R M H

25 авг. 2020 г.

I really enjoyed the course. Laurence is an amazing teacher and listening to Andrew is amazing. Thank you so much for sharing your knowledge.

автор: Rajan K

29 сент. 2020 г.

Great course to quickly add to your kitty important syntaxes to build a TF script of a Deep Learning model. Thank you Laurence and Andrew!!