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Convolutional Neural Networks in TensorFlow に戻る

deeplearning.ai による Convolutional Neural Networks in TensorFlow の受講者のレビューおよびフィードバック

4.7
428件の評価
69件のレビュー

コースについて

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 that you take the Deep Learning Specialization. The full deeplearning.ai TensorFlow Specialization will be available later this year, but you can enroll in the first two courses today. We recommend starting with Course 1: Introduction to TensorFlow for AI, ML, and DL....

人気のレビュー

MH

May 24, 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

CM

May 01, 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

フィルター:

Convolutional Neural Networks in TensorFlow: 1 - 25 / 75 レビュー

by Nick A

May 08, 2019

This course significantly lacks depth. The topic is covered at a very high-level and represents only a lightweight introduction. You will not gain any insights into the challenges that someone might face using CNNs on Tensorflow in a real-world scenario.

This course does not compare to the kind of insights that you learn from the other courses taught by Andrew Ng.

There are no graded programming assignments to validate what you have learned. The exercises that are provided are very simplistic.

by Амаль И

Jul 03, 2019

You may look at it as a set of use-cases on how to work with particular types of .ipynb notebooks or how to structure your code, but, unfortunately, lectures are useless and tasks are mechanical rather than challenging.

Huge disappointment.

by Romilly C

May 15, 2019

Excellent material superbly presented by world-class experts.

Sorry if this sounds sycophantic, but this series contains some of the best courses I've encountered in50+ years of learning.

by Asad K

Jul 04, 2019

This is the second course of the specialization and still I feel like I haven't been introduced to anything beyond the free tutorials available on tensorflow website. So far the specialization has also been only focused on the keras api of tensorflow which makes me feel that perhaps the name of this specialization has been poorly chosen (perhaps it should be 'Keras in Practice Specialization'). On the positive side, the instructor is eloquent and the learning material is presented in a well and orderly fashion (ignoring some minor cases of redundancy in notebooks; basically copy pasting the whole notebook several times just to introduce a few lines of new code).

by Ostap O

Jun 27, 2019

It is a great intro but a very limited course. Short videos and a small number of examples, for example, Transfer learning could be more in-depth. Week 4 really made a few obvious changes in the code. I do think it's great material, but all of it could be made into a 2-week course instead. Thanks for your efforts.

by Adhikari M T B A

Jun 16, 2019

Well balanced short and sweet course with practical programming exercises as well as solid theoretical background superbly presented by outstanding tech experts. Looking forward eager for next courses of this series. Thank you very much!

by Zeev S

May 14, 2019

Clear, concise, well designed

by Edir G

May 11, 2019

It's great to learn about data augmentation techniques and how to implement this. This is a great complement for the deeplearning.ai's course on Convolutional Neural Networks.

by Raffaele G

May 10, 2019

Great course! I can't wait to going further and deeper. Thanks

by Ivelin I

May 05, 2019

Many thanks to Andrew Ng and team for the great balance of theoretical background, practical references and hands-on programming exercises.

by Hoang M N T

May 04, 2019

It's a perfect course to learn TensorFlow for CNN, and it is extremely easy to understand. Thank you very much!

by Heman K

May 04, 2019

I enjoyed doing this course on CNN in Tensorflow. Thanks for the lectures by Laurence Moroney. And it is always a pleasure to hear Andrew Ng explain even difficult concepts in simple terms. He is one of my favorite teachers online, and reading about his ML course in a New York Times article back in 2012 or 2013 made me completely change my career direction and motivated me to eventually get into cloud and Big Data! And thanks also for the exercises on codelab. That makes it really convenient to learn and experiment with Machine Learning and Deep Learning.

I did take the first course in the Deep Learning Specialization early last year, but didn't get a chance to do this until now. Looking forward to completing the remaining three courses sometime this year.

by Dmitry S

May 03, 2019

Consize notebooks. Clear explanations

by Charlie M

May 01, 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

by Egon S

Apr 24, 2019

Easy to follow and very good explanations

by Antoreep J

Apr 21, 2019

In the workbook section, the question colab notebook opens up the answer notebook, please rectify the same.

by Oliver M

Apr 21, 2019

Great Course! Can't wait for part 3!

by Mahamat M A A

Jul 17, 2019

The way this course was taught very easy and clear. Thank you for both of you :)

by Shweta S

Jul 16, 2019

very good content and every point are explained nicely.

by Omar M

Jul 16, 2019

Was okay

by Renjith B

Jul 15, 2019

Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.

by Erling J

Jul 12, 2019

Brilliant course this. I especially enjoyed the parts about image augmentation with the use of ImageDataGenerator and the transfer learning addition wit huse of the Inception network.

by Santosh P Y

Jul 10, 2019

Great opportunity to experiment and learn through the exercises!

by Scott C

Jul 10, 2019

Great for people who want to not delve too deep into theory and learn the latest tools to get going quickly. I had already done the Deep Learning specialization so I recommend that as a great complement for the theory part. I learned everything I needed to get going with a practical application in this course. My only complaint is that I felt that the quizzes were poorly designed - most questions emphasized whether you remembered a specific API's argument name, or some questions were a bit ambiguous. Otherwise, highly highly recommend the course.

by Mats E

Jul 08, 2019

Very good high-level introduction course.