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學生對 Google 云端平台 提供的 TensorFlow on Google Cloud 的評價和反饋

2,676 個評分


This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models....




Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy').

Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.



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.


26 - TensorFlow on Google Cloud 的 50 個評論(共 325 個)

創建者 Abdur R M


This course is more focused on integration of other google services in TF rather then being an Intro to TF.

創建者 Richard K


The course could have more programming components.

創建者 Christopher W


I've taken a lot of MOOCs, but this has to be one of the most frustrating ones, and this includes classes with unsupported/incompatible software requirements. The instruction is minimal, so if you're expected an introduction, good luck. Questions are asked out of order and out of context in the quizes, which you can take as many times as you want. The labs are arbitrary - all you have to do is open them. The actual lab tasks -if you choose to do them- are either ridiculously easy or are unclear and require familiarity with TensorFlow and the author's mind. This last bit is the most frustrating, because there's no way that I could have gotten through the labs without referencing the solution. I now know what a tensor is (week 1) and the difference between the sequential and functional models, but I would be hard pressed to build and deploy a model to GCP.

創建者 Prem U


there is very little explaination on code in this course everything you learn is from the labs .I recommend youll to add some more explaination on each part that would be very helpful

創建者 Sotirios A L


It doesn't give too much background theory and dives straight to Tensorflow specifics without giving a smooth intro

創建者 Ankoor B


It is an easy course. The course could have been harder with graded exercises.

創建者 Kevin


Great example code to help you gain an understanding of cloud ML Engine prior to understand the deeper insights needed to make better machine learning model. Good Tensorflow overview with the estimators API along TensorBoard. It is a good end to end touch of the "ugly side" of data science people often glaze over. It's nice to see code that shows you how to scale a model because all too often online courses just teach you basic ML and nobody teaches you what to do when you run out of room on your individual computer. I would say the concepts are definitely intermediate and a prior understanding of basic ML like training, testing, predicting is needed in order the other topics of the course.

創建者 Mark B


the vid on why cloud ML was great: e.g. taking things to a new level by offering distributed training to users

the debrief of the last lab was key.

some pointers re the challenge exercises would be nice.

Great job introducing users to tensor flow, the estimators and how to train and deploy in the cloud.


What was I looking at in the ML cloud log (last lab)?

Why was there a deadline for the assignments? I thought this was self paced learning. I like the material a lot yet my day job workload varies so having deadlines on labs that I would want to understand in more depth is sometimes not ideal. In any case. Great material . Thank you

創建者 Iman R


This course introduce quite well about insight What is tensorflow, what its feature and what additional feature that you can use with gcp. One think that's minus from it's in my opinion is, sometimes the video that's explain the lab had a different version of notebook file that we use for the lab. I know maybe it's just the instructor that add more line of code to explain further instruction, but in my opinion I prefer if the instructor can explain the same with the solution lab, so we can play it more around with it's. But it's just a matter of personal taste, so it's okay in my opinion and in general this course is great

創建者 Sarwar A


It's a very good course will teach you fundamental building blocks of building a good DNN model.Though this course look small in terms of time but it has lots of assignments.

It will teach you from basics of keras sequential and functional api.Along with how to pass data in the model for training for large scale as well as small scale .This course extensibly dealt with api and how to use it effectively for training with very large data as well as small data.

創建者 Maurice E


Just start! Stick with it, and by the time you get to the end you'll have an intuitive if not race-day functional grasp of ML and the wonderful Google Cloud Platform. This is a great way to plot your first steps to ML mastery! Highly recommended by one who has survived thus far and still hasn't geeked away all my non-techie friends.

創建者 Anupam K


It is really a divine knowledge, If you are really a new to in the area of data science and especially to Tensor flow, With in a few weeks, I learnt so much and got a humongous confidence in building a model, Still long way to to go, But this course is really worth. Thanks Google Team and Coursera for putting things together .

創建者 Patrick M A


It is a great course that does not go to deep into Tensorflow, but shows exactly with what purpose it was designed and how you can use it depending on your needs. There is also very nice example code that helps you start your own projects from a very nice and clean structure. So far, the best intro to Tensorflow I have seen!

創建者 Sinan G


Amazing beginning of modelling to actual production, and a unique work flow that is relatively easy handle. Only thing I missed from the course was a more information, examples and explanation of how to actually produce and build the file.

創建者 Jafed E G


I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

創建者 Juan P D P


This course shows show how to build models in tensorflow what is necessary to train and validate them, then you can check your work using tensorboard in a graphical way. Lastly you can expose your ML model using gcloud.

創建者 Tural Y


This course is helping to understand how google tackle ML at high level. Thanks to this course you get an opportunity to play at Google AI Platform and sense the power of ML with such hi tech company as Google.

創建者 Venkata S P C


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.

創建者 Antony J


Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy').

Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.

創建者 Kris W


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!

創建者 Shen S


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.

創建者 Serge R


very useful for getting up to speed on tensorflowa little inconvenient that you have to start the infrastructure for each lab, takes minutes (5-7)

創建者 Shree K P J


Wonderful course and specilization to deep dive into ML. Take your time and work on this course with all your heart to get in to the heart of ML

創建者 Mario R


Excellent course!!! It is the best way to really understand TensorFlow and getting to know its real potential. Thanks to all the instructors!!!

創建者 Pratik S


Course is Best so far i learnt in Tensor Flow . It has all modules and content to be successful in Deep Learning and GCP Machine learning .