Image Recognition for Beginners using CNN in R Studio [Free Online Course] - TechCracked

Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio

This course includes:

• 6 hours on-demand video
• 3 articles
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

What you'll learn

• Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
• Build an end-to-end Image recognition project in R
• Learn usage of Keras and Tensorflow libraries
• Use Artificial Neural Networks (ANN) to make predictions

Description

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Recognition model in R, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

• Identify the Image Recognition problems which can be solved using CNN Models.
• Create CNN models in R using Keras and Tensorflow libraries and analyze their results
• Confidently practice, discuss and understand Deep Learning concepts
• Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

Also See : Deep Learning A-Z™: Hands-On Artificial Neural Networks

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