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Learner Reviews & Feedback for Custom Models, Layers, and Loss Functions with TensorFlow by DeepLearning.AI

4.9
stars
974 ratings

About the Course

In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. • Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions. • Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models....

Top reviews

NP

Feb 3, 2021

It is advanced TF specialization and the way contents are presented in the course are very systematically. Definitely recommended for developers already familiar with TF and wanted to explore further.

MS

Nov 24, 2020

Really great course, it teaches you all about the TF API and how to customize it for your needs, i thought only pytorch can make that as it's really pythonic, but i am a nieve noob what can i say.

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26 - 50 of 211 Reviews for Custom Models, Layers, and Loss Functions with TensorFlow

By Orson T M

Dec 5, 2020

good course , thank you deeplearning.ai !!

By Enzo D G M

Nov 23, 2020

Excellent course!!! very well explained

By onkar s p

Nov 24, 2020

Lawrence you are just awesome !!

By Tim C

Dec 22, 2020

Powerful stuff

By Dmitry D

Jan 8, 2021

Great course! Trainer clearly explains necessary features of keras, that are widely used. Very good code examples.

Just one issue: a dataset used in week 5 is unavailable in early january 2021 -- at least couple of days

By Pramit D

Jan 14, 2021

Wow! What a course it is! Amazing. Thanks to DeepLearningAi and Laurence for this course. But the mentors should be more active in the discussion forum. Not everyone is not comfortable with slack.

By Fadillah M

Jan 6, 2021

Great course for you who want to know how flexible Keras is. From this course, I realize that both Tensorflow & Keras are flexible and simple to use with.

By Ryan A

Jan 7, 2021

I started this course with the intention of learning the syntax needed to implement VAEs. This course satisfied that requirement perfectly! Thank you :)

By Imad y

Jan 8, 2021

It was a very interesting course and the teacher delivered it very well. Thanks to Coursera and its members.

By David J

Nov 25, 2021

In general, the entire course is well organized and boiled down to the most vital topics, so that the instructors could've given them in a given time. But, one thing that bothered me was the quiz. It's hard for me to tell that all quizzes were helpful for some of them were quite tricky to pick one answer or it was too specific or out of focus, in my opinion.

By Teemu K

Dec 1, 2020

Interesting content in easy to digest sized parts. Maybe a bit too much repetation (lectures and coding screencasts). Some typos like units="1", which look quite amateurish. Homeworks were too easy. Too much fill this part kinda homeworks.

By Sayak P

Nov 18, 2020

The quiz questions seemed a bit fuzzy sometimes. For example, there was a question on why do we loop through the residual blocks and the answer was to reduce the network depth. What's the context of reduction here?

By Franklin S

Jan 3, 2021

Very useful and complete course for those who want to go deeper into tensorflow tools for make customize models in their own problem.

By brahmendra

Dec 25, 2020

Very useful for developing our own networks

By Igor B

Jan 7, 2021

Ok, but Andrew NG Courses are much better

By Artem M

Jul 23, 2021

Just an overview straigt from tensorflow documentation without any practical use cases or anything that has more value than completely free youtube tutorials

By Грачев Д И

Feb 11, 2022

Too easy tasks. This course can be passed in one day easily

By Paul k

Oct 19, 2023

this instructor not only knows his stuff but he disseminates in a way so thoroughly that if you follow his prompts u cant help but acquire some of the hidden treasures that unlock machine learning - this course was an eye opener as the reverse chinese boxes of tensor flow unveil a new world of opportunities - from something seemingly as simple as pointing out how to get a look at the actual tests being performed to little gems that wil also help you bone up on coding itself - ready for course 2 - kudos - Andrew Ng is a tremendous educator in his own right he also has the ability to put the right people in the right places to help others Laurence Moroney is top notch -

By Anuj B

Aug 12, 2023

Great, Unbelievable, wonderful, From usage of functional api to creating custom loss functions,custom layers, and finally ending at custom models. Sub classing has beingmade easy in this course and from basic to advance has been covered 'really' gradually . everything great about this course, like assignments were deeply in sighted as well as well guided. Lots of lots of 'hands on experience ' in this course.

Thanks to Course era and the Team.

By Asif A

Jan 21, 2021

Perfectly structured course with clear explanations and very easy to follow examples. Learned inception network structure, vgg, block based architectures, removed a lot confusion on I had on resnets that I learned earlier. I had some prior knowledge of functional API multi IO, but learning about model subclassing, custom losses, layers, lambda layers will make it easy to implement various custom model by myself.

By Ernest W

Nov 12, 2021

The first course of Advanced Techniques specialization is great! I was afraid it could be too difficult after finishing only the deep learning specialization. It is an overview of creating custom functions and models, someone might think the assignments are too trivial but I think it's great as the author explains the concepts in understandable way. More complex tasks would be overwhelming.

By Richard G

Jun 22, 2023

This course actually solidifies what you cover in the 4 course tensorflow developer specialisation and goes slightly beyond what the Tensorflow certification handbook requires (at least round model architecture). If you wanted to go beyond sequential and functional API models and do more complex networks, then you need this course.

By Marco Z

Feb 21, 2021

The course is built really step by step with many clear examples and repetitions not only about tensor flow but also to improve the way one writes code, efficiently and clean. Quiz and Programming Tasks are not difficult as all is very well formulated and the clear. Check capabilities reduce debugging stress ...

By Akshay K

Jan 14, 2021

I enjoyed this course thoroughly. The video explanations were simple and easy to understand. I really liked the part where a video is dedicated to go through the code. Along with having hands-on access to the code which is taught in the videos, the assignments were also of great help for getting our hands dirty.

By Homayoun

Apr 23, 2021

My favorite part of this course and other courses in this and other TensorFlow specializations offer by Laurence and Deep learnign.AI is the recaps at the beginning of every video; He connects all the videos and concepts together and makes the learner understand where they are and where they're going and why.