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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

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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 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

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?

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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2176 - 2200 de 2,774 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

por Ilya R

31 de jul. de 2020

I like Laurence's teaching style. This isn't the first his course I've taken. It's nice that he has some interesting datasets of his own and some research questions. But I have a couple of suggestions to this course.

First of all tensorflow documentation has a lot (and I mean A LOT) of good tutorials so I'd expect some of them to be included in the course. That what you can expect of Google's developer advocate to do. I really need some help in understanding those tutorials.

The second suggestion - the course is far too basic. That's probably OK but we really need a follow-up course to dig much deeper into, image processing and custom metrics and losses as an example. It's really not enough background to really reproduce results from say AI for medicine courses that you may get from this series.

por John S

4 de ago. de 2020

The course does a good job of teaching basic TF functionality. It doesn't go deep into actually how NNs work which I supposed is fine if you already have that knowledge. The exercises are a little finicky when it comes to grading. My biggest hangup is that the time they calculate for each week is extremely over-estimated. Half the modules are "Readings" which they allot "10 minutes" for and most of these are a single paragraph or just a rehashing of what was just said in the previous video. The exercises also shouldn't take anyone near 3 hours to complete. So, keep in mind that each week's material can be completed in probably 30-45 minutes at most...not 6 hours!

por Avinash M

6 de sep. de 2019

I thoroughly enjoyed the course and programming various CNNs on TensorFlow. However, in certain lectures (especially the ones with the horse/human data sets), the instructor could spend some more time explaining the process of downloading and storing the training and validation images. It took me some effort and quite a lot of Googling to figure out those parts of the code. While that might not be directly related to the task at hand (binary classification) it is, in my opinion, necessary to understand some of these ancillary tasks as well. Perhaps these explanations could be included as optional videos for those who wish to understand these features of TF.

por Kaustubh D

28 de jul. de 2019

This is an excellent course to get hands-on. Keeping some tasks as repetitive like those of the callback functions help make the person strongly hands-on and remember them. Just the way, every week's programming assignment involved writing the callback function, if there would be other TF functions/methods that the coder gets to implement and override and other TF abstract classes to extend from, that would have been cherry on top!

Drilling down from the bigger picture of model definition to seemed extremely useful.

And since there are tons of courses on theory of ML and DL, thank god this one just focusses on coding it out.

por Egor E

2 de ago. de 2019

I like structure and content of this introdactory course. And like the easy and clear way Laurence Moroney told about all this stuff. Particulary, I like clear formulated exercises. During course we got great bulk of working examples in jupiter notebookes, containg full lecture, notes, likns to supporting materials!

What I would improve in course it is the change a litle bit a balance from solving problems to technical implementation. We learn a lot of using CNN for image recognition. However, it would be great to listen in more details about calculating the shape for input and outputs for layers.

por Aditya L

15 de ago. de 2020

Course in concise and to the point, I was hoping to learn more about TensorFlow than Keras. It is a good course to dive into deep learning without much knowledge in data science. The instructor is motivating and explains the concepts fairly well. I want him to improve the explanation of the parameters (e.g steps_per_epoch vs epochs vs iterations) in Keras as the course is quite applied and making these explanations better will significantly improve the course. I hope to learn more TensorFlow in the next courses in the specialization. The examples were really good and explained the concepts well.

por Renee S R

24 de jun. de 2020

Very good material and enjoyed the short videos, sample code, and ease of moving through the materials.

I like how it is broken into 4-weeks and the amount of effort seems appropriate.

I did experience some frustration with the exercise submission process. Seems whenever I clicked on the link to see my submission, more often than not the submission was not stored and I had to rewrite my solution multiple times. In the end, by clicking 'ok' in the submit response box (rather than the link), I had better luck, as it allowed me to return to the notebook and save it.

por Devansh K

10 de feb. de 2020

I loved the instructors and the content. For the first time, I found a course that actually taught me the practical aspects of deep learning in a fun and interactive way. The content was very good and the right level of difficulty, i.e. not too difficult but also reasonably challenging. One thing I would change about the course to make it better would be to have longer instructional videos that go over all the code in more detail. I did not completely understand some sections of code and I think this would have changed if there were more code explanations.

por João A J d S

30 de abr. de 2019

It's a great course! Very well structured, with an amazing amount of jupyter Notebooks (Colab) to work with, in a real hands on approach.

Just one criticism, which is why I didn't classify it as 5 Star: There isn't much of an evaluation. The tests are a bit easy, and it would be good to have at least one extensive assignment (maybe with other datasets...).

It's just that I feel the contents were really good. But if I can just pass the tests easily, I feel it doesn't really count as much of a "quality stamp" (to have passed this course).

por Jennifer J

14 de jul. de 2020

Great course, however it was annoying having to "roll the dice" so to speak to get the answers right. Perhaps if it wasn't left to chance to achieve the right grades, it would of been much quicker and easier to get through. I'd say this is also a course for everyone who's had at least some experience in programming. Understanding some deep learning helps, but you definitely need knowledge of how to code in python. If you can code well in Python and are good with math, then this course would be a breeze for you!

por Pawel B

14 de abr. de 2020

Overall the course is nice and provided me with some skills. The main drawback is that the course does not demand large amount of student's input. If you are quite familiar with Python and have some basic ML understanding, I guess you can do it in less than 48 hours (including videos, readings and assessments). The tests can be guessed, the coding exercises are better, but also largely rely on the codes provided during the course. Having in mind this was "introduction", 4 stars.

por José D

12 de abr. de 2020

Very quick and simple introduction to Neural Network using Keras's Tensorflow high-level API. Simple understandable introductory examples about how to build a neural network or Convolutional Neural Network in a few lines of code. There's no Math in this course. The downside is you won't understand how it works under the hood, and why it works (or doesn't ;-)). If you want a deeper understanding, you must study "DeepLearning Specialization" and/or "Machine Learning" course.

por Bruce B

27 de feb. de 2020

A great starter course. My only suggestions:

In the code completion exercises, a note or two indicating what is expected to be done, would be helpful. You kind-of have to go back and look at the previous problems to guess what is being asked of the student.

A complete slide deck would be very helpful, if only to be able to write notes onto the slides. And it would allow the student to do less scribbling, and more pondering of the problems being discussed.

por Samuel M

23 de ene. de 2020

Nice class, covers some basics of tensorflow and learns how to quickly build a NN. Not too fond of the quizzes: a few unclear question/choices and lots of "learn by heart" questions (like: what is the size of the pictures in this specific dataset, what is that specific param name) which you can easily answer without understanding too much. The assignments are simple enough for an introduction, quite close to the lesson examples but still interesting.

por Achal J

6 de ago. de 2020

This course is good, and is fast paced.Mr Mororney is an excellent instructor.

The course is real good and basically focuses on CNN and ANN. The only shortcoming was that they didn't really teach 'pure' tensorflow,this course is really about Keras and not tensorflow. We are taught how to use Keras with tensorflow as backend .

They should have taught basics of tensorflow such as place holders and what are tensors!.

Nevertheless, it's a good course.

por Jeramie G

5 de may. de 2020

This is a great introductory course which focuses on implementing basic Keras models. The only gripe I have with this course is the programming assignments. I experienced many, many issues while trying to submit the assignments even with proposed solutions from the discussion board. This isn't a show-stopper; just a little frustrating. Otherwise, I highly recommend this course to anyone getting started withTensorFlow & Keras model building.

por Ruxue P

18 de sep. de 2020

I've been an machine learning engineer for 2 years, taking this course to push myself to learn new TF2 features. I don't think there's focus on the new distributed training feature in TF2, codes are still TF1.x.

I think the last quiz had some really unclear questions, could be improved.

Other than that, I love the hands-on practice part. Wish we explain more on why model performance is so brittle in the horse vs human classification example.

por Amit K

12 de abr. de 2020

Although course content seems to be nice but a regular update with the current tensorflow version needs to be done. Also, In course content there are topics listed just to tell that in next section what you will study(even provided 10 mins for that) and that is a complete waste and poorly put in the content section-this needs to be fixed.

On a positive node, this course is very useful to start and I recommended this to beginners.

por Rajesh R

2 de jun. de 2020

Great review of TF and the newer tf.keras API in addition to practical advice on deep learning projects. Lawerence has been a pretty good instructor, clear and to the point, with some good exercises. For beginners, though the course skims over some of the basics - although these are covered in Andrew Ng's Deep Learning specialization, which I took some years ago. All in all, a handy course to get cracking on TF and Keras again!

por Ronet S

25 de feb. de 2020

Brilliant Course for getting started with Tensorflow. The only thing I would like the instructor to include are explanation for non -TF stuff, like matplotlib codes. It would really help develop additional skills apart from making TF models. Going online to find the working of each command in a different library like matplotlib really broke my flow and my focus . So, that would be a welcome addition!

por Jim D

25 de sep. de 2019

I really liked that it was very hands-on and made it very quick and easy to get up to speed on using TensorFlow for Machine Learning. That said, there was a lot less content that I expected (I finished the '4-week' course in about 1.5 days), and I was a bit disappointed that the focus was exclusively on image classification. A little variety in terms of the problems being solved would've been nice.

por Venkatesan A S

7 de sep. de 2020

The course is targeted for an audience with a basic knowledge of the machine learning techniques. With that in mind, it delivers a comprehensive first look at the calibre of the TensorFlow framework. For the more advanced practitioners the programming exercises and the quiz might seem a bit on the easier side. Overall it is a great place to start for those who wish to begin with TensorFlow. Kudos!

por Miguel L

11 de abr. de 2020

The contents and instructor is excellent. Unfortunately one is faced with mainly two downfalls. Fist, there always seems to be some sort of submission problem which usually involves some kind of "hacking" from the user's part to make it work. Secondly, it's very hard to get answers to most doubts since the activity in the discussion Forums is very little, specially from instructor-level sources.

por Elias B

7 de ago. de 2019

Overall a very good course (with knowledge from the deep learning specialization) to get a deeper knowledge in tensorflow. But sometimes in the exercises you feel a little left alone, because of missing information (example Week 4, which folder should you use, what's the resolution of the images, you can find out but that requiered (for me I had to downloaded the files and check what i needed).

por Andrei N

3 de ago. de 2019

The course is quite light even for introductory one. At the same time, I enjoyed examples of NN implementations in colab and elaboration on how the code works. The most interesting for me personaly were hints and techics helping to develop a better understending on how the NN are builing its knowlarge on input data. I look forward to checking other courses of specialization out.