Вернуться к Convolutional Neural Networks in TensorFlow

4.7

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Оценки: 2,301

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Рецензии: 331

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....

Sep 12, 2019

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

Sep 14, 2019

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.

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автор: Dora M B

•Aug 27, 2019

It's a great course. I enjoyed it!

автор: Chintada A

•Aug 21, 2019

really nice introduction to CNNs

автор: Zeev S

•May 14, 2019

Clear, concise, well designed

автор: NITESH N

•Aug 26, 2019

great

автор: 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.

автор: Kaustubh D

•Aug 06, 2019

This course is taught excellently, but there is very little content at least from a programming point of view. There was no need of an extra week for only specifying the differences of binary and multi-class classification in code. Rather, there could have been more covered if codes of different output structure like object recognition where the output is not a flat map could be covered. If it has been purposely done to keep the course open to even newbies in Machine Learning, then there should have been a course focussed for those who have done Andrew Ng's ML/DL specialization.

автор: Paweł D

•May 15, 2019

Pretty basic level, aimed rather to beginners.

автор: Walter H L P

•Aug 06, 2019

This course is so short in content that, in the whole last week, it is explained a trivial concept about multi-class classification. Besides, the last quiz recycle questions from the previous quizzes from this and the previous course. It is clear that the course was made in a hurry once the notebook examples lack in written content or figures explaining the subject. Finally, there is no practical assignments in this "Tensorflow in practice" course.

автор: 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.

автор: Ben R

•Dec 28, 2019

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.

автор: Hannan S

•Oct 28, 2019

First of all, the course was amazing! I found it great for the following reasons:

- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge

- The introductions by Andrew NG were really nice

- Easy to understand codes and understanding of thr underlying principles

- Varied topics such as CNN, NLP & Time Series

- Very insightful by providing expert opinions about different ways of model optimization

I really enjoyed the course and I thank the instructor for the same :)

автор: Victor H

•Oct 31, 2019

I am already familiar with machine learing and convolutional neural networks, and before starting using the TensorFlow framework I wanted to develop my own know-how in order to really have control and knowledge on what am I doing. Now that my C++/CUDA implementations work, I feel allowed to use a better tool like TensorFlow / Keras, and I am really discovering their power and flexibility, and I am getting really excited of the productivity that I can gain in my projects thanks to them!

автор: neil h

•Jul 30, 2019

Laurence Moroney presents another superb primer on the mechanics of tensor flow. Heavy on image analysis, we see how convolutional nets — concatenating stages of convolutional filters and pooling — extract features from images at whatever scale they appear. The exercises contain a modicum of basic-python skills reinforcement. Upon completion, one is equipped to tackle other common problems, e.g., the usps handwritten-digits challenge https://www.kaggle.com/bistaumanga/usps-dataset.

автор: Mastaneh T A

•Jun 03, 2019

The pace of these two courses and the extremely to-the-point nature of the explanations, examples, and exercises enabled me to implement customized CNN-based codes my own data in only 5 weeks. Now I am definitely more confident to explore and implement more complex models and concepts in Tensorflow. Thanks to Andrew, Laurence, and the rest of the team for the very efficient learning experience and for sharing their knowledge and expertise.

автор: Rishiganesh V

•Jul 20, 2019

It is really an amazing course, My heartfelt thanks to Mr.Laurence Moroney, for his great teaching and Mr. Andrew Ng for giving these great platform. I Really enjoyed the course. I learned it lot of things here. I am going to take all the specialization in these courses. And It is great pleasure to thank Coursera platform for providing me Financial aid to take up these course.

Thanks

Rishiganesh.V

автор: Himansh M

•Dec 10, 2019

This course is a great addition to the deep learning courses by Prof. Andrew Ng. I got to learn the fundamentals of deep learning from Andrew Ng's courses and learned to programme from here. It's a great course to learn Tensorflow and this course also helped me in my final year project. I'm really thankful to Coursera and deeplearning.ai for this course

автор: Eulier A G M

•Jul 17, 2019

The course is marvelous explain and with clear, concise & straight forward concepts alike the practice project.

Take your time to understand the concepts, so you can move on.

I'll recommend to watch the specialization of Neural Network from Andrew Ng, to deeply understand the "magic" ( linear regression, matrices, derivatives) of Neural Networks.

автор: Wei X

•Sep 25, 2019

I originally expected to learn more pure TF related stuff. But instead I learned Keras. Data augmentation with Keras is quite easy. Transfer learning is also easy to do if there is Keras model there already. But I do hope to learn a pure TF tutorial that are more common when you download other people's TF model and practice with your own data.

автор: Anil K S

•Jun 12, 2019

This was the actual dealing with the dataset saved at local memory location rather than predefine dataset where the dealing with label and directory were ignored which learner actually face problems while learning and handling with the datasets stored at local drive. well this course actually helped for my major year project .

автор: Ara B

•Aug 19, 2019

Easy to follow. a lot of examples. I was expecting at least one assignment for the final! :)

As for the convolution we never talked about DOG+SIFT or other feature extraction techniques. Also I would like to see how we can separate an object of interest from background e.g. using clustering or a video stream.

автор: Alvaro M A N

•Dec 10, 2019

I love this, because the instructor make the difficult easy. After ending this course, I believe I would enrolled on the other specialization, to gain a better mathematical understanding of convolutional neural networks but I'm pretty happy to learn the practical stuff, this make possible a lot of projects!

автор: anujeet

•Dec 14, 2019

This course in tensorflow specialization is a must recommended. It builds knowledge from beginners to advance very smoothly, You will be able to get a experience of how to begin coding for tensorflow also be able to understand its core layers, And learning from Laurence is always fun.

автор: Sanjay M

•Aug 13, 2019

Very well thought through course for Convolution Neural Networks using Tensorflow, covering some of advances topics like transfer learning, callback and review convolution layers. I already had understanding about CNN and these topics. This course shared scenarios when it is used.

автор: Simon Z

•Sep 10, 2019

Excellent. I learned after a couple of years working with neural networks new topics and implementations. I think it would be a good idea to include also here an exercise that gets graded at the end such that we take our time and can try out if we can make things work.

автор: Abhinav S T

•Jun 22, 2019

The week 1 is a bit casual but where as the remaining one's are just awesome learnt a lot like how to implement a model without overfiting and learnt how to implement transfer learning and multi-class classification problem, really worthy taking up this course....!!!