What you will learn in this course?
• Understand what is convolution.
• Understand how convolution is going to apply in audio effects.
• Understand how convolution is going to apply in image effects.
• Implement Gaussian edge and blur detection in code.
• Implement a simple echo effect in code
• Understand how convolution helps in image classification.
• Understand and basics and core of the architecture of a convolutional neural network (CNN)
• Implement a convolutional neural network in TensorFlow
• Implement a convolutional neural network in Theano.
About this course
This course is all about deep learning, how to use deep learning various computer visions using convolutional neural networks. Convolution neural networks are the state of the art when it comes to image classification. This neural network beats vanilla deep networks at tasks like MNIST.
In this course we are going to take a closer look at the StreetView House Number (SVHN) dataset - which uses bigger color images at various angles - so things are going to get tougher both in terms of the difficulty of the classification task and computationally as well. But will show and teach you that convolutional neural networks, or CNNs, are much more than capable of handling this task!
We are going to go in-depth on CNNs; convolution is a central part of this type of neural network and thus contains a huge value in software industry. It has more applications than you might imagine, such as modeling artificial organs like the heart and the pancreas. We are going to teach you how to build convolutional filters that can be applied to audio, like the echo effect, build filters for image effects, like the Gaussian blur and edge detection.
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