Chevron Left
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
5,550 classificações
837 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

JM

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.

RB

Mar 15, 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..

Filtrar por:

26 — 50 de 830 Avaliações para o Convolutional Neural Networks in TensorFlow

por kaushal

Aug 23, 2019

got hands on , many stuff of cnn , great content. Thank you team

por Raffaele G

May 10, 2019

Great course! I can't wait to going further and deeper. Thanks

por Asad A

Aug 23, 2019

Learnt a lot and believe me this is perfect way to teach.

por Egon S

Apr 24, 2019

Easy to follow and very good explanations

por Dmitry S

May 03, 2019

Consize notebooks. Clear explanations

por Oliver M

Apr 21, 2019

Great Course! Can't wait for part 3!

por DORA M B

Aug 27, 2019

It's a great course. I enjoyed it!

por Chintada A

Aug 21, 2019

really nice introduction to CNNs

por Zeev S

May 14, 2019

Clear, concise, well designed

por NITESH N

Aug 26, 2019

great

por Nicolas

Aug 30, 2019

First, I think the course was great, very instructive. Thanks to Andrew and Laurence for putting this together, is a great source of information to understand more about DL. Some things I think could improve the course.

I found the transfer learning lessons a bit unclear and I struggle generalizing this to other cases. Also, I was a bit confused by the flow of the course. The course starts with a multi classifier (or actually, the previous course), then the lessons focus on binary classifiers and it ends again with multi classifiers, because these should be the more complex ones.

One last technical thing, only on the last lesson of this course it is mentioned that the classifiers output the probabilities on alphabetical order when using ImageDataGenerators (or at least, that's my impresision). I've wondered since the course introduced the ImageDataGenerators, how the probabilities are assigned on the outputs. I could figure out on the sigmoid that the classifier would look for the first class on the directory and output 1 or 0 based on that, but it would be good to have this mentioned at some point on the video when the ImageDataGen is introduced.

Thanks again! Great course

por José D

Apr 18, 2020

We go into deeper details following Course 1 with Convolutional Neural Network, using Data Augmentation & Dropout to reduce over-fitting, and with only a few lines of code thank to Keras (TensorFlow high level API). Easy useful examples. Just like Course1, there is no math, so you cannot understand what's under the hood, how and why it works. If you want deeper understanding, you must do the "Deep Learning" specialization, which is harder than this specialization.

por Jorge L M B

Aug 10, 2020

I liked the hands-on approach of the course, but felt that the last assignment (Week 4) was a little buggy into which parts of code to write and which ones not. Nonetheless, I had a lot of fun!

por 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.

por Vedang W

Apr 18, 2020

The course has some great parts such as augmentation and transfer learning, but my expectations were understanding Tensorflow at a deeper level.

por Oleg K

Aug 07, 2020

Last assignment could have been explained better. Laurence does not talk about ImageDataProcessing.flow, despite this is the only solution

por Zoltan S

Aug 02, 2020

After taking Andrew Ng,s truly excellent 5 course specialization, I was hoping that this followup specialization would be at the same high level. In my view (and I am sad to say this) the present course doesn't live up to that expectation.

Of course you could still learn something useful, mostly a selected part of the Keras API. The instuctor is friendly and explains some of the basics of convolutional neural networks. If you are willing to experiment on your own (run the code longer on Colab, play with the hyperparameters, etc) then you get more practice and certainly more out of this experience. Keras has a lot of good tools. For more advanced students going directly to the TensorFlow tutorial website is also an option (and it is free).

Overall the course seems a bit rushed, while it has the potential to be better. Let me suggest adding more basic materials to solidify knowledge (for example practicing hands-on image preprocessing before teaching the Keras preprocessing API and overall more experimentation with images). Also adding more exercises on more diverse topics (GAN's, face detection, variational autoencoders, object detection).

There are also some minor issues (easy to fix): for example right now in the Week 3 HW the prepared callback teaches the students exactly the wrong approach. It stops the learning cycle when the training accuracy improves over a certain threshold, instead of checking the validation accuracy. That is an unfortunate mistake to make in a week that discusses different ways to avoid overfitting.

por 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.

por Deepak A S

Apr 17, 2020

This course doesn't talk about tensor flow. But uses keras only. The title is misleading!

por Md. M R

May 21, 2020

Good, but not so good. they could have introduced tensorflow 2.0s functional api

por Paweł D

May 15, 2019

Pretty basic level, aimed rather to beginners.

por Jarrod H

Mar 23, 2020

The lectures are really good and quite engaging. The extra course content by Dr. Ng is also generally where I learn the most. This class does a decent job in introducing to you the Tensorflow library.

However...

It feels a bit like an very well done tutorial. After finishing my second class I don't any any more idea of how to build a neural network than I did before. The data that they give you has already been cleaned and is ready to use which never happens in real life. The data manipulations that they ask you to do in the homework has zero explanation of why you are doing it. Just that add an extra dimension or split the data this way. I don't know why we need it split that way and it never says why. Further, exercise 4 in particular uses a different method than has been used for the entirety of first and second class. You're given a list of numbers rather than real images.

At the end of the class I wanted to understand how to build these models in the real world. If I want to predict cats vs dogs then great! However, if I want to try to categorize financial transactions or predict fraud or literally anything else, this class gives you no understanding of where to start, or how to approach the problem generally.

por Pawel B

May 01, 2020

The course does not provide much knowledge. In fact this is a tiny extension compared to first part ("Introduction to TensorFlow for..."). The assignments are trivial - you need to change one or two parameters from the excercise codes. But another story is that the codes you succesfully run in Jupyter need to be tailored to satisfy coursera system (e.g. data cannot be loaded due to out of memory, TF version is different etc.). Instead of working on the model, you loose time on experimenting with the code unrelated to tensorflow or look at the forum for solutions specically suited to pass the test (they do not change how the models works). Do not recommend this course.

por Jobandeep S

Apr 21, 2020

the exercises are not very challenging and most of them are the same as the practice colab notebooks, there should be more variety. And also the grading in the exercises is not good there are a lot of errors and it should be made more robust to individual changes made by the student

por Roberto E M C

May 01, 2020

Very shallow and full of typos! And the staff doesn't care.