Latest Courses
ISTQB Artificial Intelligence Tester Sample ExamsCheck course
JAVA Programming Online Practice ExamCheck course
Programming for Kids and Beginners: Learn to Code in PythonCheck course
Practice Exams | Codeigniter 4 developer certificationCheck course
WordPress Practice Tests & Interview Questions (Basic/Adv)Check course
Git &Github Practice Tests & Interview Questions (Basic/Adv)Check course
Machine Learning and Deep Learning for Interviews & ResearchCheck course
Laravel | Build Pizza E-commerce WebsiteCheck course
101 - F5 CERTIFICATION EXAMCheck course
Master Python by Practicing 100 QuestionCheck course
ISTQB Artificial Intelligence Tester Sample ExamsCheck course
JAVA Programming Online Practice ExamCheck course
Programming for Kids and Beginners: Learn to Code in PythonCheck course
Practice Exams | Codeigniter 4 developer certificationCheck course
WordPress Practice Tests & Interview Questions (Basic/Adv)Check course
Data Pipelines with TensorFlow Data Services

Data Pipelines with TensorFlow Data Services

FREE

Add your review
Add to wishlistAdded to wishlistRemoved from wishlist 0
Add to compare
8.2/10 (Our Score)
Product is rated as #211 in category Machine Learning

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this third course, you’ll use a suite of tools in TensorFlow to more effectively leverage data and train your model. You’ll learn how to leverage built–in datasets with just a few lines of code, use APIs to control how you split your data, and process all types of unstructured data. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Instructor Details

Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. As the author of more programming books than he can count, he's excited to be working with deeplearn.ai and Coursera in producing video training. When not working with technology, he's a member of the Science Fiction Writers of America, having authored several science fiction novels, a produced screenplay and comic books, including the prequel to the movie 'Equilibrium' starring Christian Bale. Laurence is based in Washington State, where he drinks way too much coffee.

Specification: Data Pipelines with TensorFlow Data Services

Duration

13 hours

Year

2020

Level

Intermediate

Certificate

Yes

Quizzes

Yes

12 reviews for Data Pipelines with TensorFlow Data Services

3.3 out of 5
4
1
4
0
3
Write a review
Show all Most Helpful Highest Rating Lowest Rating
  1. Sayak P

    Very practical!

    Helpful(0) Unhelpful(0)You have already voted this
  2. Soren J

    Some issues with notebooks. This is still in beta. Absolutely no help with the technical setup (notebooks and the Tensorflow datasets). Needs to be debug a couple of times..

    Helpful(0) Unhelpful(0)You have already voted this
  3. Danilo C

    I’m sorry but, it does not seem realistic pipelines, it clearly show the capability of tensorflow, but real world data pipeline on my point of view is completely different from that. I was expecting something like how to handle large amounts of data coming into the cloud, or onpremise cluster, and get it into a retraining pipeline, improving the models… but was completely different… If you are expecting something like, How to retrain a large model with large amounts of new data, realtime… that is not the course for you. I love Andrew and Lawrence, but this last specialization is not at the same level from the other 3 from Deeplearning.ai, you guys should consider rethinking it using more Cloud deployment strategies with Tensorflow, like delivering APIs that requests model inference, and retrain automatically, using Google Cloud, Sagemaker, Azure whatever…, integrate it into a MLOps/DevOps model, and delivery at scale, at edge, that is the real world of deployment in my view…

    Helpful(0) Unhelpful(0)You have already voted this
  4. Michael

    The course has a lot of practical experience and content. The reference material available, including the support is very limited. Which makes it hard to debug the code, you would literally spend days. I struggled on my on with no help whatsoever from the mentors in week 3. At least in week 4, there was some help. The balance between the quiz questions, which does not contribute in any way to the overall passing, and the practical is totally off. Maybe if we could get notes, to help us. Maybe just touch ups, but overall, Mr Laurence Moroney you are a great trainer. Looking forward to course 4.

    Helpful(0) Unhelpful(0)You have already voted this
  5. Andrei D

    Excellent course both for Data Scientists and Machine Learning Engineers!

    Helpful(0) Unhelpful(0)You have already voted this
  6. Thomas A

    The last exercise does not seem complete. There is too less help about solving the excercise moderators do not help.

    Helpful(0) Unhelpful(0)You have already voted this
  7. Qi D

    good,but the last exercise is a bit tricky

    Helpful(0) Unhelpful(0)You have already voted this
  8. Liang Chun C

    It’s a more advanced topic related to creating datasets which fit into TensorFlow data pipeline. However, lectures contain too much information per slide without highlight what the instructor was talking. A little bit hard to follow. Overall, This course include useful information and require additional time to organize all materials again. Thanks for making such an incredible course.

    Helpful(0) Unhelpful(0)You have already voted this
  9. Evgeny K

    Unfortunately this course is extremely weak. Tons of poorly explained code and nothing else

    Helpful(0) Unhelpful(0)You have already voted this
  10. Andras G

    Dataset creation task was more complex for me then all previous before.

    Helpful(0) Unhelpful(0)You have already voted this
  11. Cees R

    I liked the topic and instruction of this course. I had bumped onto the notion of datasets earlier, was impatient as I needed to just resolve an issue, and skipped it. Next time I know what they are about and will be able and happy to use (including build) them. Slight minus: presentation in the video often contained some bullets that I couldn’t connect to the speech, that is, I had to choose: read or listen. Bummer: the last week’s exercise effectively required to copy paste from a notebook that was scrolled through in the video. That is silly enough in itself. What is more, for certain errors in the created code in the notebook, the grader gave a standard notification that was not helpful in resolving the identifying what coding error had been made. As the discussion showed, a good number of people me including had been struggling with this to the level of feeling helpless to resolve it. Still four stars for instructional value of the whole course, but I hope for the sake of future students that the above mentioned exercise will be replaced by a better one.

    Helpful(0) Unhelpful(0)You have already voted this
  12. Nicholas B

    This seemed very helpful and hands on. I can’t wait to try this on my own.

    Helpful(0) Unhelpful(0)You have already voted this

    Add a review

    Your email address will not be published. Required fields are marked *

    This site uses Akismet to reduce spam. Learn how your comment data is processed.

    Data Pipelines with TensorFlow Data Services
    Data Pipelines with TensorFlow Data Services

    Price tracking

    Java Code Geeks
    Logo
    Register New Account
    Compare items
    • Total (0)
    Compare