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Opiniones y comentarios de aprendices correspondientes a Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning por parte de deeplearning.ai

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15,432 calificaciones
3,228 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. 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

AS
8 de mar. de 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

RD
13 de ago. de 2019

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

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2576 - 2600 de 3,217 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

por Michael F

31 de mar. de 2020

I like Mr Moroney's videos. He is very good in explaining things. Sometimes he goes a bit too fast through the notebooks. It would be very nice if he could dive a bit deeper into the mathematical/theoretical backgrounds. The theoretical sources for reading are good, but I find it sometimes very helpful if somebody is explaining it. Thanks.

por Jack P

11 de oct. de 2020

Great intro to getting started with TensorFlow. Highly recommended to do this after getting the basic theory from the deeplearning specialisation (i'mm the kind of person that likes to understand at least at high level what is going on so would say do the deeplearning first. However if you prefer to do the learn after could do it second!)

por Shehryar M K K

13 de abr. de 2020

This course focuses on the practical programming aspects of deep learning networks rather than the theory. It is very helpful to get hands-on training with tensor-flow. I would highly advise taking Andrew Ng's Deep Learning course before taking this course as it makes understanding fundamentals of deep learning more meaningful and fun.

por Lukas A

23 de mar. de 2020

Very good to start some coding and get hands-on experience. However, they do not go into depth about what the parameters mean and why it makes sense to set them just as they do it in the instructions. It gives you a great experience, but I am not sure if all this is useful when working with real data on real problems with real pitfalls.

por Kristopher J

1 de sep. de 2019

The course has a lot of good material, and is a great follow-up to the more theoretical Deep Learning Specialization.

However, I can't give it five stars because the exercises are a bit repetitive, and the quizzes have some very poorly worded questions. I know this is a new course, so I hope they can smooth out some of these rough edges.

por AbdulSamad M Z

31 de jul. de 2020

Excellent course to get a grasp on the basics of ML/DL with Computer Vision on TensorFlow. Lecture delivery is super clear and the exercises nicely complement the material and give you the hands-on work you need. Having taken Andrew Ng's Machine Learning course will significantly help as this course is more practical than theoretical.

por Rishabh C

20 de jun. de 2020

Good starting point to learn about training and deploying Deep Learning Models using Tensorflow. Some concepts cause a little confusion regarding the import structure of these libraries. Sometimes only import tensorflow is used while sometimes keras is imported as well. But all the code uses tf.keras , so it gets a little confusing.

por Xuanlong Y

9 de nov. de 2019

It's good enough for beginners, but I have to say it's still a little bit easy. Maybe teacher can give us more reading materials or show us more interesting projects that we can reach after this Specialization and I think that can be called an introduction. But anyway, I think the course is better than many tutorials on the Internet.

por Arpit G

21 de dic. de 2020

Overall the course is very good . It gives a simple introduction to Deep Learning especially ConvNets. I would have loved if some of the concepts like Convolution were explained in more depth using some cool animations. The good thing about this course is that it never appeared to be a burden , rather it was a joyful experience.

por Hessel C W

21 de mar. de 2020

This course offers a very solid introduction in tensorflow for CNN applications. One comment I had is that the quizzes did not test for deep understanding at all. They were more things you'd remember literally from the text and explanation, rather than new questions that can only be answered with some deeper understanding.

por Adarsh K

30 de may. de 2020

It's really a good course for hands-on in Deep learning using TensorFlow and Keras.

It's going to be great fun with very short videos and getting more focus on practical aspects.

This course is very precise and I really liked it and recommend for everyone who have their interest in AI, Machine Learning, and Deep Learning.

por Kaan A

14 de ago. de 2019

With this course I learned new things on tensorflow. However, It feels like it is very very simple introduction to tensorflow. After finishing Deep Learning Specialization I thought that this Specialization will be complementary. I'm disappointed a little bit on the first course. I believe other courses will be better!

por Rafael R C

25 de mar. de 2019

Me gustó el curso, muy explicativo y lleno de recursos, aunque solo rasca la superficial. Hay muchas cosas mas por aprender. Los foros no reciben respuesta tan rápida. Si bien es cierto google Colab, ayuda a esta tareas, cuando se quiere pasar al computadora personal se complica por versiones o librerías faltantes.

por Wyatt M

8 de sep. de 2020

Completed course - most issues I ran into involved submission related issues as I was running everything alongside on my machine and was successful. Part 4 also seems a little lacking in the middle for hands on time compared to the other sections, but I was able to figure it out by taking notes during the videos.

por Nikolay R

11 de sep. de 2019

Very, very basic course for absolute beginners. It makes sure you know enough to build and train models for simple image recognition tasks. 4 weeks is a crazy long period for it, though. I finished it in 2.5 days (I have previous exposure though), but even a beginner should be able to do it in 1 week.

por Ayush K

8 de may. de 2020

It is quite a good course. But you need to finish the additional materials along the way to really understand the purpose of parameters in different sequential layers. But that's only if have never studied DL. If you have, then like me you can rather focus on the computational features of tensorflow.

por Mashood M M

5 de dic. de 2019

This course although thought me the basics of DNN but there can be improvements in the code (notebooks) and in the videos as they only tell the abstract part of the concepts. Overall the course was great it helped me alot from knowing what is keras to the journey of building my own model. Thanks

por Vivek G

3 de may. de 2020

I expected that they would teach "Tensorflow" not Keras(its high level implementation), If youre here for learning tensorflow then maybe you should refer to some books. Overall the content is good if are new to Deep Learning and want to learn keras. Thanks Andrew-ng Thankyou Laurence Moroney.

por Aman G

22 de mar. de 2020

Wish it was a little more in depth about things that it taught. It was a very high level overview. Considering that it's a beginner, may be that is how it should have been. But I personally would have liked to learn things in-depth. Kudos to having us do lots of practical assignments!

por Vaibhav G

1 de dic. de 2019

The course content was really good. But as greed for more never ends, it would be great if we could be able to shed some more light on few gray areas like in what situations we should go for 64 filters or 32 filters, how to determine size of the hidden layer in terms of neurons etc.

por pavan b g

15 de mar. de 2019

Thanks to Laurence and Andrew for the sharing there knowledge which have all the foundation requires to get the AI understanding using Tensorflow , keras. Simply awosome .

This is really a refresher for those who already are into datascience field, even for the aspiring students too.

por Renzo B

19 de abr. de 2020

This is a good introductory course for using Tensorflow. If you have finished the deep learning specialization, you will easily breeze through this course. It is an overall great course however, I feel that the instructor could have discussed the concepts a little bit deeper.

por Amit G

26 de may. de 2021

This course is great, it starts with beginning and slowly moves upwards but there is a lot of room for improvement such as the reading time in the weeks is total unnecessary and it accounts for almost 4-5 hours, and quizzes are way too easy and so were the weekly exercises.

por Antoine J

2 de ago. de 2020

Can be hard to figure out what needs to be done in the exercises (excepet week4). Also it would be great to have more resources available to understand the underlying maths behind some of the algorithms. Other than that, good intro to the TF library (mainly keras for NNs)

por Sathiya N C

7 de may. de 2020

decent course to begin with, but doesn't take you into the details of all the parameters, functions used. Instead focuses more on solving the problem easily through Tensorflow. Could be better if given the rationale behind using all functions, choice of parameters etc.