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Convolutional Neural Networks in TensorFlow

Convolutional Neural Networks in TensorFlow

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9.1/10 (Our Score)
Product is rated as #35 in category Machine Learning

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 course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open–source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real–world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. 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 …

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: Convolutional Neural Networks in TensorFlow

Duration

8 hours

Year

2019

Level

Intermediate

Certificate

Yes

Quizzes

Yes

68 reviews for Convolutional Neural Networks in TensorFlow

4.3 out of 5
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  1. grover a

    VERY GOOD COURSE

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  2. Ben R

    This was just an exceptionally well done course. It’s not complicated, but I don’t think the point of it is to be complicated, just practical. All in all, I enjoy the teacher’s style. If you’re trying to understand the fundamentals of the theory and mathematics, these courses aren’t for you; if you’re looking to just gain a practical and useful working knowledge, then this is a great starting place. I took it to just round out my understanding of Tensorflow via Keras; this was a great course for that.

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  3. Amardeep S

    The homework assignments should be required and more challenging

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  4. Ambroise L

    What could improve it: Not enough depth in the practicals if you have already done Andrew Ng’s course on Conv nets. No graded practical exercise. What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,

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  5. Shuhei O

    fantastic material and instructor

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  6. Ahmet K

    Nice course! All detailed and explained. Thanks!

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  7. June L

    It’s a lot of fun building a ConvNet using my dog’s pictures and reduce the overfitness through augmentation, dropouts and transfer learning! Thanks!

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  8. Lydia N

    Amazing course, a fast overview on how to facilitate TensorFlow for computer vision problems.

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  9. kripa s

    Very informative for those who are new in tensorflow

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  10. Shashank T

    This will help in understanding the basics and will enable the individual to work effectively.

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  11. Henrique C G

    I’m sad to say that I’m really disappointed with the course. What is even stranger is that professor Andrew is associated and endorse the course. I like professor Marooney, but honestly, even his free tutorials on the Tensorflow channel on Youtube have more information than this course. It really seems like something put together in a haste just to make it available on Coursera. The level of detail and instructions is not on par with the quality of both the Coursera platform and the professors associated with this course. It seems that as I progress through the courses in this specialization the instructions get poorer and poorer and the level of information gets more and more scarce. It got to a point where we are just given notebooks to run; they are not even graded (they barely were on the first course). And even the notebooks where the we are given a chance to complete some code, there are absurd things like “print(#your code here#)” in places that don’t even make sense except if we copy and paste from the other notebooks of the course. Really? Print what? The only way we can guess what kind of debug info the notebook is asking is by looking at other notebooks at that exact same line. For the reviewers; if you are really reading this, please remember that Coursera is charging $49/month for this specialization. If we consider that an average student will take 4 weeks to complete, that’s almost $200 for something that’s barely a tutorial at it’s current version. $49 may be a reasonable rate for a citizen of the US, for example, but it’s and exorbitant amount of money for students of poorer countries using the platform in hopes of acquire knowledge of decent quality.

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  12. Hanan S A

    code and slides are nice and crystal clear

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  13. Mouafek A

    not much of insights or details, and it’s too easy!

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  14. Ajithavalli C

    It was a great course! I am not an image processing person, but I enjoyed learning about CNNs through them. Looking forward to applications on texts!

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  15. Renato R

    needs to be more advanced too basic

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  16. Gursimran S

    very very useful and powerful toll i learn 🙂

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  17. Yuliya M

    It’s a wonderful course! Pity that the data for the fourth exercise are not available anymore.

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  18. Bhabani D

    Great introductory course to learn the application of TensorFlow with Keras in the field of Convolutional Neural Network.

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  19. Muthiah A

    Useful continuation for practitioner.

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  20. Nitesh S

    i was able to complete my minor project after completing this course

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  21. Gregor F

    Straight to point with a lot of useful techniques! Interesting examples as well.

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  22. MOHAN S S T

    great techniques with one line of code great!!! …

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  23. Sai K K K

    Fundamentals Concepts and Coding related to CNN Classifications, Augmentation, Dropouts, Regularization and Transfer learning are well presented. Enjoyed a lot during the course..

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  24. Hadi F

    Very good course

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  25. rajesh t

    Need more depth and real life scenarios.

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  26. Vishal S

    Nice course

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  27. hitashu k

    AWESOME

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  28. Saoudi H

    very weeeeelllll 😀

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  29. Rakesh G

    I think this was a good course but the standard of exercises and quizzes was too easy. More conceptual questions especially in quizzes would help in understanding the topic better

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  30. Mohammad Z

    Exceptional

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  31. Li P Z

    If you have taken Andrew’s courses in ML or deep learning, you will be disappointed. The amount of content in the videos and exercises is shrunk down by 75% per week. I think a much better job could have been done of structuring the course, and creating meaningful exercises. The instructor does an OK job of showing you how to use TF, but he doesn’t always explain things very clearly, and doesn’t always have an accurate understanding of how ML or deep learning works.

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  32. Barka T

    I learnt a lot through this course, object detection and recognition with tensorflow made easy

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  33. Rushikesh W

    Greate course

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  34. clement l r

    A very nice course to finish understand well convolutio, data augmentation, overfitting in neural network, as well as transfer learning and making classifier for binary and multiclass.

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  35. Aditya W

    I mainly to learn the various constructs to do various things in TensorFlow, and this course is very well constructed for it. It doesn’t explain the actual mathematics though, and I don’t blame it for that. It is just designed to help people learn the framework. Overall, a very satisfying experience.

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  36. Fadhel A

    I hope it will be upgraded with graded weekly test (case study and building some models).

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  37. Jonathan P

    Very nice course!

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  38. David P

    Laurence does a great job. Very easy to understand. Andrew, as always, provides great insight.

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  39. Ana C

    Very interesting course. I’ve learned a lot. I would like to have a little more advanced topics

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  40. Vitali

    Great course on CNN

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  41. Philippe B

    Tres bon cours sur les reseaux convolutionnels.

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  42. Victor C

    implement example is helpful, hope I can use these in my try

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  43. Artem D

    I liked the lectures (videos). And I did not like that the course has no mandatory programming assignments. I pay for the course to make myself study. And I believe that there is no study without practice. Hence, this course did not make me study, thus I don’t understand why I need this course : (. And I could find free lectures about TF/Keras (maybe not so good, but free) and/or read the documentation. BTW, I really like Andrew NG’s courses, but this one really disappointed me.

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  44. Kellen B

    Really explains the material thoroughly and efficiently. I really feel like I understand the use cases / applications from this course.

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  45. Rishabh B

    Could have had tougher exercises. Great basics course nevertheless!

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  46. Nantogma S

    This is an excellent course that got me up and running on CNN and TensorFlow. Thanks to the instructor

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  47. Siddharth C

    Awesome Course

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  48. Chitresh K

    Loved the content and depth of the materials discussed

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  49. Rushikesh N P

    this is a great course,teaching is outstanding. this course has covered all basics needed for deep learning and CNN.this course also give us good understanding of tensorflow.

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  50. Carlos D R

    Es una gran continuacion al curso anterior. No se puede hacer sin haber hecho previamente el otro curso

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  51. Michael E

    Would like to have seen some information on techniques such as batch normalization and residual layers.

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  52. Michael E

    Would like to have seen some information on techniques such as batch normalization and residual layers.

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  53. Sachin K M

    The Way this has been driven, I never felt disconnected.

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  54. Sachin K M

    The Way this has been driven, I never felt disconnected.

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  55. Eyal B

    Didn’t provide a real understanding for transfer learning

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  56. Eyal B

    Didn’t provide a real understanding for transfer learning

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  57. Isaac A

    excellent course! very informative and very well organized and delivered!

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  58. Ignacio R L

    Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings

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  59. Sarayut N

    This course is really great!

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  60. Bob K

    As another reviewer mentioned, this course is much simpler than Andrew Ng’s deep learning specialisation but even so it has it’s uses. I’m taking it to prepare for the Google TensorFlow certificate and it’s forcing me to learn more of the api. Andrew Ng’s course was how to implement the theory from papers, whereas this course is how to use TensorFlow. Each has it’s place, although the former is probably more valuable.

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  61. Petyo R P

    Great on the point training. It gives a lot of food for thought.

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  62. Huy N

    Very useful

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  63. Takanori N

    Good Course!!!

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  64. Selcuk K

    Great course. Thank you, Laurence. However, I should admit submitting the exercises is a pain sometimes. Discussions help though.

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  65. Sajal C

    Course is good however there can be programming assignments for better practice.

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  66. Ruichen Y

    a starting point to get hands dirty on using Keras.

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  67. Ricardo F

    Excelent course! The examples could be more elaborated, but this is a minor issue. Congratulations, and thanks a lot!

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  68. Suresh K M

    Very nice!

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