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Voltar para Convolutional Neural Networks in TensorFlow

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

4.7
estrelas
6,934 classificações
1,081 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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901 — 925 de 1,079 Avaliações para o Convolutional Neural Networks in TensorFlow

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 Sung-Hyun S

25 de Ago de 2021

Good!

por S. M S H

21 de Set de 2020

Good

por Anastasia C

8 de Abr de 2021

I had a problem with the weekly assignments 1 and 4, which I think asked for things that were not presented at all in the videos. On the first week, without any preparation, we were asked to create the directories, supposedly without any python background, which was tough. But that was just a couple of commands. In the last weekly assignment, the file reading and loading was very problematic for those who hadn't previous python knowledge, and pretty advanced too. In the course of the lessons and the notebooks shown previously, never had we encountered .csv files, and the way to load the images that we were introduced to, with directories, was not at all present in the final project. Also, the methods that were needed afterwards (.flow(), evaluate) were not even hinted before, even in the comments of the assignment, if not before, during the videos/notebooks. All in all, the last exercise took me by surprise and was really tough to get working, because the course was almost irrelevant to it (no transfer learning or directories, the two main points in the course).

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.