Chevron Left
Voltar para Intro to TensorFlow

Comentários e feedback de alunos de Intro to TensorFlow da instituição Google Cloud

4.4
estrelas
2,192 classificações
251 avaliações

Sobre o curso

We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine...

Melhores avaliações

DW

Oct 17, 2018

pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!

SS

Jun 06, 2018

Nice introduce, might be more on introduce the model structure, because I still need to read additional notes to locate how to train my deep learning model online.

Filtrar por:

126 — 150 de {totalReviews} Avaliações para o Intro to TensorFlow

por 江祖榮

Sep 19, 2019

Good

por Fathima j

May 11, 2019

good

por Dong H S

Apr 28, 2019

good

por Atichat P

Jun 02, 2018

Good

por Girish S K

Jul 22, 2019

The course was good introduction to tensor flow I learned lot of basics which otherwise I could not have learned from books or other online materials. The concepts are well explained. What I am not happy is about the Datascience labs. In places where internet is slow it is very difficult to do it. Instead of this in we are provided some alternate instructions to run them on a local machine that would have helped at least for some of the first few labs. I know that all of them cannot be run on local machine then the whole purpose of learning tensorflow on Google Cloud is defeated. The whole purpose is to learn how to run it on a cloud environment with scaling. I know that is not possible on a local machine. Another option would be to provide instructions to run the code with without notebook. I basically do not like notebooks , I Prefer command line to notebooks to execute and see results live. But overall I got a good intro about tensorflow - Thankyou very much.

por Benny P

Dec 05, 2019

First of all we need to understand that TensorFlow is not just a Python toolkit. It's a complete tools from Python library, training management, monitoring, down to deployment to cloud or what have you. Therefore this course should be viewed as getting started introduction to ALL of that, not just the toolkit. And I think it's quite good. There are few glitches here and there when it comes to interacting with the GCP, but that's fine, you're learning something while fixing it. The disappointment comes from the forum though, as the staff's only response seem to be to shift the responsibility to Qwiklabs

por Yaron K

Jul 14, 2018

An excellent introduction to TensorFlow, Including debugging tips, and how to scale up TensorFlow models and deploy them. So why only 4 stars ? because there is no audit option for this course and the videos can't be downloaded. Presumable the notebooks with sample code can be cloned from Github - but it seems the explanations will not be available unless you re-enroll. This policy is even more inexplicable considering that the course serves as a "presale" for the Google cloud platform.

por David M B

Feb 26, 2019

Very useful but I had some problems with lab infrastructure. Options to create buckets wouldn't appear sometimes and I had to open and close google cloud console to make it work sometimes. Regarding the course it was great but there is a lot of boilerplate code and though the steps are simple and clear there is a lot to digest, I will need much more time master this TF/GCP workflow, but anyway this is a great start.

por Sachin A

Jun 16, 2018

I think a lot of the lab-explanation given in the video following the qwiklab should be in the python notebook; make it a little more illustrative (e.g. architecture diagrams). Also, be a little more generous with the lab time - the last lab was too long (or perhaps change the code to select the faster ML option - standard/TPUs etc. to make the training go faster)

por Zhenyu W

Jan 20, 2019

One of the lecturers should improve his English speaking. The course should add more contents, explanations, and exercises for the 3rd part of the course regarding how to scale TF models with CMLE, for example, some bash cmds or some code are confusing, unless this content will be covered more in the following courses.

por James S

Apr 20, 2020

I could not get my final lab project to work. I have sent the issue to Qwiklabs - I got the following error message:

ls: cannot access '/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/03_tensorflow/labs/taxi_trained/export/exporter/': No such file or directory

por Thibault D

Sep 10, 2019

I enjoyed this course a lot. If I could modify anything, I would adjust the content and pace of the third week. The videos are relatively simple to understand and well-explained while the final lab feels a lot harder with a lot of unknown command to execute.

por Asmit M

Jul 30, 2019

hands on demonstrations were good. More in depth explanation can be done fro some of the codes including the part in which data fatching from the json file was explained, and the process to be followed in the gcp to make the model and deploy it.

por Carlos V

Jun 24, 2018

Excellent course in the capabilities of tensorflow, the course material and data-lab examples are super useful and provide a good overview of how to implement tensorflow models locally and in the cloud with high-quality practices.

por Benjamin B

Sep 26, 2018

Challenge problems at the end of each assignment are really good, however, there should be videos showing how the instructors would solve them, I would be fine watching 30 min videos describing the solutions. Nice course!

por K R V K

Mar 30, 2020

Intro to TF should have packed with more fundamental concepts around TF alongside existing topics covered. Moreover, some of the code needs either further explanation or references to understand what a given code is for.

por Bartosz C

Apr 23, 2020

There were some technical problems. Some of the exercises could be described in more detail with TODOs.

Nevertheless I very much enjoyed the course. Quite an amount of material. Challenging tasks. You can learn a lot.

por GAURAV B

Feb 13, 2020

Was expecting a bit more around tensorflow basic concepts. Coverage was too much from basic to production level deployment. Was expecting a bit more hands-on on tensorflow basics and details around deployment.

por Loucas L

Aug 31, 2019

The tools and methods presented were great. The instructors were also fantastic.

However the coding exercises were lacking in guidance even though the complete solution is given in the video.

por Tom W

Mar 31, 2020

This needs some updating - looks like the Tensorboard is no longer accessed in the same way as it was when this course was produced.

Otherwise great! Good challenges! :/

por Yuvaraj G

Apr 05, 2019

The procedure to connect to the cloud datalab was time consuming to do it every time.

Suggestion : More topics in Core Tensorflow could be added. I enjoyed the course!

por Quoc B D

Jul 05, 2018

Good general TensorFlow introduction. The course focuses on the highest level tf.Estimator whereas there are maybe something interesting low level they don't present.

por Misho K

Mar 09, 2020

The course covers quite a few concepts -- TF basics, TF estimator, Google Cloud ML. It would be easier if the material is split into TF and Google Cloud lessons.

por Tyler B

Oct 07, 2018

Great course as an introduction to TF, however, the labs are not as in depth as I'd have liked. Nonetheless, the course is well executed by the presenters.

por Stefan K

May 11, 2020

A good understanding of bash cmds and a well-digested understanding of the course material is required to perform the labs. Quite challenging.