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Learner Reviews & Feedback for Convolutional Neural Networks in TensorFlow by DeepLearning.AI

4.7
stars
6,036 ratings
918 reviews

About the Course

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. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. 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....

Top reviews

JM
Sep 11, 2019

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

MS
Nov 12, 2020

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

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601 - 625 of 912 Reviews for Convolutional Neural Networks in TensorFlow

By Gaurav P

Aug 8, 2019

Too basic

By Mohamed M F

Aug 2, 2020

Amazing!

By Christos M

Mar 21, 2020

Perfect!

By Shailesh K

Aug 4, 2020

Awesome

By Vaibhav v s

Jun 2, 2020

Awesome

By Mazahir R

Apr 17, 2020

Perfect

By Santiago G

Mar 12, 2020

Thanks!

By hitashu k

Jan 13, 2020

AWESOME

By Kavya B

Dec 5, 2019

awesome

By Jeff L J D

Nov 5, 2020

Thanks

By Josef N

Jun 19, 2020

great!

By Ben B

May 6, 2020

Great

By Aji S

May 3, 2020

great

By Johnnie W

Sep 22, 2020

good

By RAGHUVEER S D

Jul 25, 2020

good

By Rifat R

Jun 7, 2020

Good

By PANG M Q

May 29, 2020

good

By Amit K

May 13, 2020

Good

By Nho N

Mar 17, 2020

good

By zhenzhen w

Nov 18, 2019

nice

By Jurassic

Sep 6, 2019

good

By 林韋銘

Aug 20, 2019

gj

By João A J d S

Aug 3, 2019

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

By Anand H

Sep 12, 2019

One challenge i have faced is with deploying the trained models. I find very little coverage on that across courses. It's one thing to save a model.h5 or model.pb. It would be nice if you can add a small piece on deployment of these models using TF Serving or something similar. There is some distance between just getting these files outputted and deploying. TF documentation is confusing about some of these things. Would be nice if you can include a module on that.

By AbdulSamad M Z

Aug 1, 2020

Great course! Builds on the concepts of Course 1 in this Specialization although the course can be taken without having completed Course 1. Concepts are explained in a super clear and engaging way and the hands-on exercises give you the experience you need to become proficient. The course covers plenty of practical concepts including some pitfalls for practitioners to avoid, but the theoretical concepts are covered less than I expected.