Convolutional Neural Networks in Python: CNN Computer Vision

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A brief description

Are you looking for a comprehensive Convolutional Neural Network (CNN) course that will teach you everything you need to know about building an Image Recognition model in Python?

You’ve come to the right place to learn about Convolutional Neural Networks.

You will be able to do the following after completing this course:

Determine can Image Recognition issues can be addressed with CNN Models.

Create and evaluate CNN models in Python using the Keras and Tensorflow libraries.

Train, discuss, and comprehend Deep Learning topics with confidence.

Understand advanced image recognition models include LeNet, GoogleNet, VGG16, and others.

How would this course benefit you?

Both students who complete this Convolutional Neural Networks course will receive a verifiable Certificate of Completion.

If you are an analyst, ML scientist, or a student interested in understanding and applying Deep Learning to real-world image recognition issues, this course will provide you with a strong framework by showing you some of the most advanced Deep Learning principles and their application in Python without going too mathematical.

Why do you enroll in this program?

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.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. We have helped companies address their business challenges using Deep learning approaches as managers of a Multinational Analytics Consultancy company, and we have used our expertise to incorporate realistic elements of data processing in this course.

When it comes to data mining, machine learning, and deep learning, what’s the difference?

Simply put, machine learning and data mining use the same algorithms and techniques as data mining, with the exception that the types of forecasts are different. Machine learning reproduces known patterns and knowledge—and then applies the information to data, decision-making, and actions—while data mining finds previously unknown patterns and knowledge.

Deep learning, on the other hand, employs sophisticated computational power and specific forms of neural networks to study, understand, and classify complex patterns from vast quantities of data.

This course is intended for the following individuals:

Those interested in a career in data science
Professionals who are only starting out on their Deep Learning journey
Anyone who wants to practice image recognition from the ground up in a brief amount of time.

 

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