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Voltar para Introdução ao TensorFlow

Comentários e feedback de alunos de Introdução ao TensorFlow da instituição Google Cloud

4.4
estrelas
2,457 classificações
297 avaliações

Sobre o curso

This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform....

Melhores avaliações

VC

May 18, 2020

I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.

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!

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151 — 175 de 292 Avaliações para o Introdução ao TensorFlow

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 Simon Z

Jun 05, 2020

At a couple of important points in the course (e.g. where it is about launching TensorBoard or even more important where it is about deploying the model with ML Engine) the code in the Lab differs substantially from what is shown in the discussion of the lab. This is a little irritating. That aside, I have learned a bunch of new techniques and processes to improve my coding and especially: code more quickly and scalable. Thanks for some really good lessons.

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 Vijay 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 andy g

Jun 27, 2020

The first week was the best, as it described some of what's going on under the hood. I would have liked much more on these topics and less on specific cloud products

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 APARUPA M

Sep 24, 2020

Very nice Course for beginners to TensorFlow. Thank you Professor.

It would be helpful if correct answers for last lab be provided so to understand more.

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.

por Dimitry I

Nov 06, 2019

Good course. Familiarity with gcloud command should be a prereq. Thank you to Coursera and the Google team for putting it together.