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Convolutional Neural Networks in TensorFlow

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.In this four-course Specialization, you'll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network's performance using convolutions as you train it to identify real-world images. You'll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input

4.5
248 reviews

Created by: Laurence Moroney

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Overall Score : 90 / 100

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Course Description

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.
Read More product length iconTime: 8h

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Instructor Details

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Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. As the author of more programming books than he can count, he's excited to be working with deeplearn.ai and Coursera in producing video training. When not working with technology, he's a member of the Science Fiction Writers of America, having authored several science fiction novels, a produced screenplay and comic books, including the prequel to the movie 'Equilibrium' starring Christian Bale. Laurence is based in Washington State, where he drinks way too much coffee.

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Reviews

4.5

248 reviews in total

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By Karunanidhi M on 16-Nov-19

I think most much of the course conent was same as the first course, this course could have been a little more advanced. But overall a great place to start.

By Muthu R P E on 15-Nov-19

Practice with various data sets, and learn the tools to use for convolutional neural networks with tensor flow

By Phuoc H L on 14-Nov-19

More exercises should be available for students to practice and test their skills.

By Marc-Antoine G on 13-Nov-19

Please make the "Ungraded assignment" Graded and add more comments/directive in them to make sure we understand each steps.

By Bingcheng L on 12-Nov-19

quite easy

By Hiten S on 11-Nov-19

Excellent CNN Course. This Course Covers All The Topics Like Data Augmentation, Transfer Learning, Drop-Out, And Multi-Class-Classification Problem.

By Dirk H on 7-Nov-19

If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.

By Enyang W on 6-Nov-19

too easy! not much to learn actually. all the videos could be one lesson only

By SOURAV S on 6-Nov-19

Thank you for your good explanation

By John G on 6-Nov-19

Interesting and useful. Thanks!

By Thomas L on 4-Nov-19

Maybe a bit repetitive, when you just finished Course 1. We see a lot of lines of codes explained again from course 1 and I think that could be avoided.However, the new concepts are nicely introduced and very interesting to implement!

By Sailesh G on 2-Nov-19

Expected a lot more in this course from the Tensorflow specialization. Something that'd take us beyond tf.keras.

Showing 12 of 248 reviews

Convolutional Neural Networks in TensorFlow
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  • Video Duration: icon 8h
  • Price: Free
  • Provider: icon Coursera
  • University: icon deeplearning.ai
  • Language: icon English
  • Certificate: icon Yes

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