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Comentários e feedback de alunos de Convolutional Neural Networks in TensorFlow da instituição deeplearning.ai

4.7
estrelas
6,061 classificações
924 avaliações

Sobre o 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....

Melhores avaliações

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..

MS
12 de Nov de 2020

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!

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776 — 800 de 918 Avaliações para o Convolutional Neural Networks in TensorFlow

por Kumari M

17 de Jun de 2020

Pretty much enhanced my skills

por Cheng H Z

17 de Dez de 2019

Too little things were covered

por Lorenzo G R N

9 de Dez de 2020

Course is missing classnotes

por Subhendu R M

12 de Ago de 2020

A nice well-balanced course.

por Rohit K S

18 de Set de 2020

Mind Boggling Experience!!

por Walter G

29 de Nov de 2019

A very brief quick course.

por Guilherme R M

10 de Jun de 2019

Bom curso, muito prático.

por Loutzidis A

16 de Mar de 2020

The quiz were quite easy

por Prabesh G

23 de Mai de 2019

Okey.. So easy but okey

por Tanguy C

24 de Abr de 2020

Thanks. Enjoyed it.

por j_lokesh

15 de Jun de 2020

that's was awesome

por Patrick L

26 de Dez de 2019

I like this course

por Vivek S

24 de Jun de 2019

Super cool stuff!

por Paulo A C

23 de Abr de 2020

Great content!!

por ashraf s t m

31 de Jul de 2019

Voice is low

por Venkatesh S

2 de Dez de 2019

Excellent!

por Bingcheng L

12 de Nov de 2019

quite easy

por Suraj

11 de Fev de 2020

Too easy.

por Hamzeh A

6 de Ago de 2019

Very Cool

por Omar M

16 de Jul de 2019

Was okay

por S. M S H

21 de Set de 2020

Good

por Henrique C G

1 de Jan de 2020

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.

por Michael

26 de Jul de 2019

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

por Muthukumarasamy S

4 de Ago de 2019

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

por Pablo A

4 de Set de 2020

It's a nice next step after the first course in this series, however, I think a lot of this could be summarized in a shorter course or even added to course 1. I was particularly annoyed by some of the assignments as they required knowledge of other libraries that are not part of the course. Particularly Week 2 and 4, I spent a lot of time figuring out how different libraries worked just so I could preprocess my data before even gettin on to the course material. Week 4 in particular feels cramped up and the assignment uses a lot of tools not previously discussed, I don't think I learned much from it, I just wanted to be done.