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Are you interested in learning about Convolutional Neural Networks (CNN) and how they can help you solve image classification problems? Join us for a 1.5-hour practical hands-on session where we'll explore why we need CNNs over standard Artificial Neural Networks (ANNs) and learn about the differences between CNNs and ANNs for image classification tasks.
We'll dive deep into the components of CNNs, including convolutional layers and pooling layers, and demonstrate how they work in an end-to-end image classification project using TensorFlow/Keras. You'll gain practical experience in building and training your own CNN and learn how to apply it to real-world problems.
In addition to this, we'll also discuss some state-of-the-art CNN architectures for solving large-scale object detection, object tracking, and image classification problems. By the end of this session, you'll have a solid understanding of how CNNs work and how they can be applied to solve real-world problems. So, come and join us on this exciting journey, and take your first step towards understanding how to turn pixels into insights!
Prerequisites: A zeal for learning new technologies, and good have a basic knowledge of Data Science.
Note: E-Certificates will be provided within 24 - 48 hours of the session only to those who have attended the entire webinar. Please join the Zoom webinar with your correct name and email address to ensure that your certificate is properly credited to you.
Prashant Sahu
Manager - Data Science Training at Analytics Vidhya
Prashant Sahu is a passionate Corporate Trainer & Consultant with over 10 years of experience in Data Analytics, Machine Learning & Deep Learning applications. He is currently working with Analytics Vidhya as Manager for Data Science Training and is finishing his PhD from IIT Bombay.
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