Sequences, Time Series and Prediction(으)로 돌아가기

4.6

655개의 평가

•

105개의 리뷰

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....

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.

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:

교육 기관: Michael B

•Oct 20, 2019

I absolutely love the deeplearning.ai courses. I previously took Andrew's course that went more into the theory, and this course was a fantastic compliment to it that focused more on putting deep learning concepts into practice via tensorflow. I look forward to any future courses they put out! :D

교육 기관: Eslam G

•Jul 31, 2019

This Course is very useful for beginners.

교육 기관: Eli S

•Aug 02, 2019

I was looking for a basic step by step guide to Tensorflow and this course was amazing. I can now use my knowledge in DL from Deep Learning course better. The instructor was great, explained everything clearly. I think it was better if there was programming assignments too.

교육 기관: João A J d S

•Aug 03, 2019

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

교육 기관: jbene m

•Aug 04, 2019

I have learned something. But it still too easy.

교육 기관: Parth A

•Aug 11, 2019

A good intro course to time series prediction. Would have loved some more data analysis and other time series methods like ARIMA and seasonal ARIMA

교육 기관: Jesse S

•Aug 12, 2019

A little bit too simple cuz it only covers univariate time series practice. Would be better if there's more multivariate time series exercise.

교육 기관: William G

•Aug 16, 2019

Though I feel some aspects of this course did not delve deep enough into the explanations of some functions, the course helped me understand how to use models for time series problems.

교육 기관: CM

•Aug 19, 2019

Wish there were graded programming exercises. The quizzes has questions not relevant to the goal of the lesson ex What is the seasonality of sunspots.

교육 기관: Manish G

•Aug 22, 2019

Good course it gives you quick start on practical aspects of Deep Learning techniques. Nice!

교육 기관: Saeif

•Aug 24, 2019

I wish there was graded assignment as the quizzes are shallow and not enough to practice.

교육 기관: Egor E

•Aug 24, 2019

I like very match the first and second week of the course, because it contains dense new theoretical and practical things. The idea of time series forecasting and preparing windowed dataset was explained very clear and was very usefull for all next lessons. Also the difference between statistic and neural network approaches was very helpful.

The 3 and 4 week I would prefer zip in one , because the experiments with RNN, LSTM and Conv is very familiar and actually I've done them together one by one. I would pleased to learn some explanation and examples why each type of architecture follow their result. How the results depend on dataset preparation. Particulary, I did not get what architecture work better with seasonality, autocorrelations, and noise.

교육 기관: Pak S H

•Aug 30, 2019

It will be better if there is also a multivariate time series example.

교육 기관: Amarendra M

•Sep 06, 2019

I think this course will be of great help if one has worked on time series data. I was a complete novice to time series, and found it difficult to relate. However, I learnt a great deal about the tensorflow technical aspects.

Thanks Lawrence for making it so easy :)

교육 기관: Alfonso C

•Sep 20, 2019

The course is great, but I would have loved knowing more about how to deal with multivariate time-series, data sets with many time-series, variable prediction horizon etc.

Hope a more advanced course on time series forecast with tf.keras is under construction! ;-)

교육 기관: Michalis F

•Oct 01, 2019

overall good... but was expecting a bit more of it , being the last course in the specialization

교육 기관: Devwrat N

•Sep 30, 2019

Every step in programming should have been explained in detail

교육 기관: Александр З

•Oct 01, 2019

I would like to have more info on window and batch sizes - seems to be pretty important values to work with, but they are not covered in depth.

In general, greate course that shows how to prepare sequences, feed them in to NN.

Loved it.

교육 기관: Xiang J

•Oct 07, 2019

I think overall it is a good course, these are the things I learnt:

First-hand experience with tensorflow, but more focus on the basics of keras

Knows how to preprocess data for image, text, and times series to feed it into NN

Knows basic concepts of keras layers such as CNN, LSTM, RNN, Conv1D, DNN

Knows learning rate rough gauge techniques

Things to improve:

Fix the typos, such as window[:1], there are a few posted in the forum

Should introduce more basics of tensorflow instead of kerasShould

include more links/documentation for the side knowledge, such as paddingAdding

some layers seems magical, such as Conv1D before LSTM for time series, what is the logic behind?

교육 기관: Yemi A

•Aug 17, 2019

I found the start of the specialism was very well explained; and as a result now I really understand CNNs (as it is was explained much better than the other courses I’m doing on Udemy and LinkedIn Learning). However I would suggest that Andrew and Laurence revisit the latter part of the course from a learner point of view, looking at the pain points along their journey through Sequences and Predictions. Overall, the structure of the whole specialism can be improved, and I find it not as good as my previous course (Andrew’s Standford University Machine Learning Course which was brilliant)

교육 기관: Masoud V

•Aug 23, 2019

Good

교육 기관: Super-intelligent S o t C B

•Aug 26, 2019

Good course, but seems a bit hastily put together. I really liked the technique for determining optimal learning rate. Thank you Mr. Moroney and the entire Coursera team.

교육 기관: Alejandro B G

•Sep 04, 2019

Teacher is not anywhere close to Andrew, plus the grading tools are non-existent. It goes too heavy on preprocessing when we want to learn tensorflow, you could've spent all that time in teaching Tensorflow appart from Keras.

교육 기관: Ethan V

•Sep 06, 2019

This is a good introduction to the API of keras, but that's not what I would expect from a "Tensorflow In Practice" Specialization. This is really an "Introduction to Keras" specialization, and really theory light one as well. As a graduate of the Deep Learning specialization, I expect this to be a way to apply that theory to large datasets and to novel architectures requiring some leverage of the lower level tensorflow APIs. Although I thought this course was well made, I feel it was not ambitious enough for it's name.

교육 기관: Gerardo S

•Oct 01, 2019

a little bit to light