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Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера deeplearning.ai

4.7
звезд
Оценки: 5,784
Рецензии: 876

О курсе

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....

Лучшие рецензии

MS
12 нояб. 2020 г.

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

JM
11 сент. 2019 г.

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.

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101–125 из 870 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Tanay G

8 апр. 2020 г.

I found the course really interesting and I learned a lot. The thing I liked the most about this course is the minimal helping nature of the evaluative notebooks, deep learning specialisation's notebooks practically spoon-fed the answers.

автор: arnaud k

25 июня 2019 г.

The practical aspect of this course is addicting. I can't stop myself from wanted to try the next technique. maybe because i have seen most of these before but i going had made it clear what i was doing wrong in some of my "failed kaggle"

автор: Jafed E G

6 июля 2019 г.

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

автор: Carlos V

6 июля 2019 г.

Excellent course, in particular, all explanations to work with the Image Augmentation libraries, I enjoined the transfer learning part, highly recommended for anyone looking to improve their knowledge of Convolutional Neural Networks

автор: Zeeshan A

25 июня 2020 г.

The specialization covers brief introduction to the concepts of Computer Vision and Natural Language Processing. It introduces to TensorFlow and gives a hands-on practical experience over the tool through simple assignments.

автор: Vishakan

22 апр. 2020 г.

Learnt a lot of new things about image classification, how to better predict images using TensorFlow. Laurence Moroney is a great teacher who skillfully explains the code and its significance in an easy-to-understand manner.

автор: Surya K S

5 апр. 2020 г.

Incredible course structure. Really well designed and thoughtful. The programming assignments were especially very helpful. Grateful to Coursera for letting me do this specialization during these uncertain times of COVID-19.

автор: Sawyer S

22 июня 2020 г.

Very instructive and practical, but the coding assignment can be mis-leading from time to time. However, that is not anything out of ordinary, practitioners should expect some level of sophistications in real life

автор: Low W T

9 авг. 2020 г.

Coming from an aspiring Data Scientist, Laurence Moroney provided succinct explanation on practical aspect for CNN, which is a definitely a supplementing course material alongside Deeplearning.ai's specialisation.

автор: Chirag G

8 мар. 2020 г.

This specialization is really helpful. I had done other specializations and Machine Learning Course of Andrew Ng. But this course helped me to revise those topics as well as implement them in the real world.

автор: Sharan S M

22 окт. 2019 г.

After finishing this course, I was able to build a neural network that could identify different types of boats with around 94% accuracy. I used many techniques learned in this course like image augmentation.

автор: Vincent H

26 нояб. 2019 г.

IT is a great course about Deep Learning and above all, how to code it with Python.

It is very practical and you learn a lot of features about the Tensor Flow framework that you can reuse for other issues.

автор: MD. A K A

28 мар. 2020 г.

Really enjoyed the course. Thanks deeplearning.ai team. Except "Inception" every topic was clearly practiced. For "Inception", I am eager to learn how to lock a model & how he trained weight can be saved.

автор: Mcvean S

13 нояб. 2020 г.

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

автор: Ravi P B

15 мар. 2020 г.

Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..

автор: Javier M

12 сент. 2019 г.

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.

автор: Tamim-Ul-Haq M

6 окт. 2020 г.

Excellent and detailed on how to create a convolutional neural network using TensorFlow as well as explaining how to solve problems such as low accuracy, overfitting and even improving the dataset.

автор: Akshar A K

4 июня 2020 г.

Laurence Moroney is the best. Before taking up the course, i didnt know anything about the AI or ML or Tensorflow. The concepts were explained in such a manner that anyone can learn Tensorflow.

автор: Mohd S K

14 дек. 2019 г.

a very nice course on ConvNets. Image journey through convnets and logic behind using specific type of layers. you can very wellunderstand the keras structure to build convnets through this.

автор: Ajay C

19 июня 2020 г.

Loved it. It was surely targeted for beginners first two weeks assignments were easy last two assignments had some work to do. But most of the Hints/answers are available in the comments.

автор: Vishnu N

1 авг. 2020 г.

I gained a hand full of knowledge from this course.

This course has given me a very good insight into convolution neural network work.

I thank Laurence Moroney and Andrew Ng for this course.

автор: maryam m

26 окт. 2019 г.

Well structures course. No matter your level of expertise you can learn from this course and implement models more professionally and improve answers accuracy using this course techniques.

автор: clement L R

22 янв. 2020 г.

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.

автор: Md. S R

31 июля 2019 г.

This course is so best for the new practitioners! Because when you are learning deep learning theoritically, this course will help you at such level to make you practice these highly.

автор: Erling J

12 июля 2019 г.

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.