Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.
In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs). - Import Key libraries, dataset and visualize images. - Perform image normalization and convert from color-scaled to gray-scaled images. - Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs. - Improve network performance using regularization techniques such as dropout.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Very nice course, everything was explained perfectly. Can also add about testing the trained model using external data, like if we want to give an input and perform prediction then how it is done.
Instructor was efficient in delivering the knowledge and I understood it very well. The exercises were also great. Overall, my aim for taking this course had been accomplished.
Thank you so much for such an awesome course ryan ahmad sir. I got 100/100 from your teaching. I wish i could meet you personally.
Course misses detailed explanation but its good for those who have just learned CNN's and want a quick hands on experience.
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