Chevron Left
Zurück zu Customising your models with TensorFlow 2

Bewertung und Feedback des Lernenden für Customising your models with TensorFlow 2 von Imperial College London

4.8
Sterne
140 Bewertungen
52 Bewertungen

Über den Kurs

Welcome to this course on Customising your models with TensorFlow 2! In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence 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 a custom neural translation 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 follows on directly from the previous course Getting Started with TensorFlow 2. The additional 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, CNN, RNN, ResNet), and concepts such as transfer learning, data augmentation and word embeddings....

Top-Bewertungen

DN

4. Aug. 2020

The lectures are clear and the coding assignments are very relevant and practical. The final project is complex but it is very rewarding once you complete it.

AG

12. Aug. 2021

Great Course, Got a lot to learn. Few things can be presented well especially in the 3rd the 4th week lab. Rest everything is good.

Filtern nach:

51 - 55 von 55 Bewertungen für Customising your models with TensorFlow 2

von Rob S

22. Okt. 2020

Interesting course. However, I didn't find the videos as clear as Course 1.

von Anup K

10. Juni 2021

Excellent Course .. but with difficult passing levels.

von Shaokai Z

16. Mai 2022

The content of the instructions seems unstructued. The introduction of every lessons lasts only a few minuts. The coding tutorials offered by the GTAs are neither insightful nor explanatory, where the learners are simply required to type along. Theere are also consistency issues the way the code, so that many manners can be confusing for beginners.

The assignments are not designed with the resources in mind in that, some of them require an increadibly long time to train, unless it's migrated into colab, where other issues like loading pretrained models and datasets need to be taken care of. Also, the instructions are at times vague, and the problems can only be seen once graded. No or not enough efforts were made for step by step checks and guides to help the student.

Personally, I would recommend the deep learning specialization from deeplearning.ai, which is organized way better.

von Max K

31. Jan. 2021

Never recieved my grade. Contacted coursera support. They demanded another motnh of payment for the course or else I will not get my grade. Terrible practice and this is used to punish people that finish their course early since coursera will simply wait until you pay another month before the start grading your paper!

von Jaesub S

30. März 2021

The course is not maintained. Do not waste your money. Many of the assignments are impossible to finish due to the broken grader and lack of support.