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Bewertung und Feedback des Lernenden für Getting started with TensorFlow 2 von Imperial College London

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463 Bewertungen

Über den Kurs

Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop an image classifier deep learning model from scratch. Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of Tensorflow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x. The prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP/feedforward and convolutional neural networks), activation functions, output layers, and optimisation....

Top-Bewertungen

MM

24. Jan. 2021

I already knew the subject, so I was able to go fast, but I really loved the completeness of this course, the approach, the tests, and the capstone project. Basically everything. Very good indeed!

AA

17. März 2021

Provided clear and useful insight into TensorFlow 2. Before the course I had read many of the TF2 guides and tutorials. This course helped solidify my understanding of core TF concepts.

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51 - 75 von 157 Bewertungen für Getting started with TensorFlow 2

von Islomjon S

4. Dez. 2020

It was incredible experience to study TF 2 with this course. Progressively, we studied each component of Tensorflow to build eloquent ANNs. However, it was very shallow application of Tensorflow in just using CNN (which they explained 100%), it would be very good if they also showed some other architectures too.

von Andrew H N

24. Juni 2021

G​etting Started with TensorFlow 2 was a great course -- focused, relevant, professional, and highly value-added -- thank you, Dr. Kevin Webster, and the Graduate Teaching Assistants, for presenting it! I am looking forward to completing the next course in this Coursera specialization! Best Regards, Andrew

von Woosung K

5. Mai 2021

You need to have some experience in numpy before taking this course. In particular data preprocessing is challenging without such experience, I would say. Other than that, everything is excellent. Especially I like that I can run codes using CoLab/Jupyter notebook without installing all dependencies.

von Goh K L

13. Dez. 2021

This course is a good complement to the courses offered by Deeplearning.ai in terms of focusing on the basic neural network coding. I like that the exercises in the Jupyter notebooks were left empty for students to type the codes. This encourages the students to pay attention the tutorial videos.

von Akash M

23. Sep. 2020

Intended for intermediate level students, is seriously one of the best courses with the right amount of rigour and testing. Thorough coverage of TF2; seriously would love more of such courses (apart from the specialisation) from Imperial College in this ever expanding field!

von Arish A

10. Juni 2021

Great course, i loved the fact that instructors were showing the use of Docs and the assignments in the course were nice. I like that the couse assumes a prior knowledge of Deeplearning and does not repeat them in great details here.

von Yevhen D

27. Feb. 2021

I recommend this course:

1) A lot of practices: assignments, notebooks, capstone project.

2) Theory videos are very clear and compact.

3) Authors don't try to teach you ML in 5 weeks, but instead require ML knowledge and focus on TF.

von David H

6. Apr. 2022

E​xcellent introduction to Sequential models in Tensorflow 2, very clearly presented with well-designed tutorials, and covering a lot of useful material. Assumes a little (but only a little) previous knowledge of neural networks.

von Juan C S S

26. Mai 2022

Such an amazing course! Explanations are concise and clear, and the labs are always a good opportunity to apply the new content. Weekly and final projects are great, and actually have a real application.

Great teachers as well!

von Gael H

5. Apr. 2022

E​xcellent course along with course#2. Very clear and useful explanations about using Tensorflow in a professional settings. These courses are real lifesavers given how terrible is Tensorflow documentation.

von Thales G

7. Apr. 2021

Good course. Short and very practical. It is not a basic course, as you need to know some aspects of Machine learning and Deep Learning. It opened my mind for other possibilities of use of neural networks.

von mausci71

25. Jan. 2021

I already knew the subject, so I was able to go fast, but I really loved the completeness of this course, the approach, the tests, and the capstone project. Basically everything. Very good indeed!

von Rob S

22. Okt. 2020

Excellent course! The project assignment provides a very good way to self-assess and see whether you really have understood the course material. It's a strong recommendation from me!

von Anudeep D

22. Dez. 2020

One of the best courses that i have taken on coursera. Clearly explanation of concepts and very good labs which give data scientist clear path to train models using tensorflow 2

von Hazem A

25. Nov. 2020

Excellent Course .. One of the best for practicing Tensorflow . Great content and well designed assignments ... GTA did a good job though sometimes the accent is not very clear

von Fabio K

5. Nov. 2020

Very good training. I certainly learned a lot and am getting used to this framework already. Some theoretical background in ML is highly recommended before taking this course.

von Gergely S

14. März 2021

Working along with University PhD students is very helpful! Also, the explanations from Kevin are very detailed with good visualization (highlighted source code lines).

von Arash J

10. Dez. 2020

it is an awesome course for anyone with knowledge of deep learning to learn tensorflow. looking forward for other courses in the specialization to learn more.

von Fortià V

11. Mai 2021

This is a great course to gain experience using tensorflow 2 and also to reinforce the concepts of Convolutional Neural Networks. I strongly recommend it.

von Ricardo D

23. Feb. 2021

Very good course -- explained the basics of tensorflow 2; very confident at this point that I can start developing my own tensorflow/keras applications.

von Fernando S

13. Nov. 2020

Awesome course, the best basic Keras course at Coursera, it should be more promoted, after so much time using TensorFlow, I've just found it now.

von Ton P

12. Apr. 2021

Very nice course, especially when you are already familiar with the deep learning concepts and just want to know how to code them in Tensorflow.

von Ajay A

22. Nov. 2021

I​ndeed intermediate level course. Useful course. Well designed, focussed, clutter free, comprehensive. Rare to find such course on Coursera.

von LRAV

27. Okt. 2020

This course is terrific! All you need to start coding almost any DL model. Really good to get yourself comfortable with tensorflow.

von Shine B

10. Jan. 2021

A well structured and useful course. I definitely recommend it to anyone who is searching for a solid introduction to TensorFlow.