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Opiniones y comentarios de aprendices correspondientes a Convolutional Neural Networks in TensorFlow por parte de deeplearning.ai

4.7
estrellas
6,937 calificaciones
1,081 reseña

Acerca del Curso

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....

Principales reseñas

JM
11 de sep. de 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.

RB
14 de mar. de 2020

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..

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201 - 225 de 1,083 revisiones para Convolutional Neural Networks in TensorFlow

por Kellen B

30 de ene. de 2020

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

por Johan A M M

28 de dic. de 2020

I have learned a lot, this course is very good, just one comment, please check the calification system, always failed in the last test

por KISHOR

18 de ago. de 2020

I learnt a lot of augmentation and image processing methods using tensorflow and several other tools for computer vision applications.

por Houssem A

24 de jul. de 2019

The course is well structured and explained from trainer I feel that I have more information and get knowledge in tensorflow practices

por Mohanad A N

31 de may. de 2019

I hope all courses to be like this course or like andrew's ones. Very clear, easy to follow along, tons of info, direct to the point.

por Jorge M Z P

8 de dic. de 2020

A sequential and easy to understand course. Contains all the basics to learn how to construct and improve Conv Nets on Tensorflow 2.

por Pablo C

6 de abr. de 2020

Excellent explanations, and a lot of interesting and practical examples. Recommended if you have some experience with ML literature.

por Aguirre M

2 de mar. de 2020

Excellent hands on course... if you have previously taken a more theoretical neural network course (for example Andrew Ng's classes)

por Prashant J

1 de abr. de 2020

It's been a great journey about Convolutional Neural Networks. The most interesting thing of these course is Exercise on notebooks.

por Loveprit S

12 de jun. de 2020

This course is useful to those who have basic understanding of computer vision and want to know how they can use TensorFlow in it.

por Selçuk K

31 de mar. de 2020

Great course. Thank you, Laurence.

However, I should admit submitting the exercises is a pain sometimes. Discussions help though.

por Juan S

30 de nov. de 2020

Genial el curso. Muchos ejemplos prácticos y usables para todo tipo de situaciones. Me gusta mucho como explican los profesores.

por Pushkaraj J S

11 de jul. de 2020

Great course. Introduces you to various data generation techniques which can be really useful when dealing with smaller dataset.

por 邹波波

24 de sep. de 2019

The course is very great! You can see how convolution works, image processing, transfer learning and so on. Thank you teachers!

por Norifumi I

13 de dic. de 2020

Although the topics are getting more complex, I liked how the lecturer keeps it simple and relates each data to the real world.

por Akhil K

22 de jun. de 2019

This course worked as a great reference for my project on Neural Networks. This is one of the great and well-structured course.

por Gustavo A

10 de ago. de 2019

I enjoyed this course a lot, but I missed the code evaluation step. On the other hand, the content was as good as it has been.

por Dustin Z

27 de jun. de 2020

Fun course. A good balance of easy and challenging material. The lessons based primarily on notebooks, and are very hands-on.

por Jay H

15 de jun. de 2020

It was a very helpful course. The discussions forum help a bit. Maybe, more questions asked or answers could help even more.

por Diego O S

12 de ene. de 2021

Really clear and with the exact amount of examples. (I would like to see more examples and not only the ones seen in class)

por VAGHELA H N S

18 de jun. de 2020

course is well designed. The codes and notebooks provided in the course can be very much helpful for any beginner to learn.

por Tze C L

18 de jun. de 2020

Very insightful and involving course that teaches you how to perform image classification with Convolution Neural Networks.

por Marcin Ś

30 de ago. de 2021

Pretty good introduction to CNNs with Tensorflow. Great idea with exercise assignments to finish milestones in the course!

por Bonthu S

22 de abr. de 2021

Simply the best course to implement convolution neural network in tensorflow but don't look for deep understanding of CNN.

por Zarinah C

20 de feb. de 2021

One of the best online courses I've taken. Well explained, accessible, and references so that you can deep dive as needed.