Skip to main content

Convolutional Neural Network Courses

Take free convolutional neural networks courses to build your skills in machine learning on edX today!

What is a Convolutional Neural Network?

ConvNet or CNN is a class of deep learning neural networks. They're used effectively in image recognition and classification, giving computer vision to projects heavy with imagery. They also provide "vision" to things like robots and self-driving cars or anything that would need to process visual data to function. This kind of image recognition relies on fully connected layers of neurons, but the assumption is that data is visual. Regular neural networks may not always understand the input layer of images, but with CNN, your system is primed to understand those hidden layers.

How Does CNN Work?

Convolutional networks rely on 3D architecture - height, width, and depth - to scale for image recognition. Data is fed into the input layer and then processed through a series of hidden layers before revealing the solution. The solution is the final or output layer. It expands machine learning by working through each previous layer to create the classification. There is a sequence of layers that process the data. Instead of each layer being fully connected to all the others, the three layers scale down the complexity to allow for image recognition. The convolutional layer, pooling layer, and the fully connected layer work together to reduce the image to a set of class scores. The input image goes through each layer until classified at the outset. The number of parameters can vary, and from the first layer to the output layer, the process is unsupervised. CNN Courses and Certifications Deep neural networks are critical to working with images in the era of visual data science. For a comprehensive look at how deep learning works and applies to some of our biggest data questions, look for IBM's professional certification in Deep Learning offered in partnership with edX.org. You'll gain an understanding of how CNN works by working with elements of convolutional layers, including pixel values, receptive fields, object detection, and matrix multiplication. Other courses offer specific frameworks for developing your CNN, including IBM's courses in TensorFlow, Python, and PyTorch for deep learning. Because robotics is the newest deep learning application, University of Pennsylvania's introduction to Vision Intelligence and Machine Learning can help you understand how state of the art image classification is helping create the robotics applications of the future.

Ignite Your Career with Convolutional Neural Networks

Thinking of working in robotics? The next iteration of Natural Language Processing? Do you understand that visual data is a vast untapped resource for business and want to fill the gaps? Building your knowledge of CNN is vital to understanding image data. Visual recognition will allow machines to transform manufacturing, production, transportation, and just about every other field. The building blocks for true robot vision lie in convolutional neural networks where image data can fill in gaps for operation. The input data is too large for regular neural networks, so take advantage of this specialized knowledge by following in the footsteps of Alex Krizhevsky, Matthew Zeiler, Christian Szegedy, and Yann LeCun. Your expertise could be the next big breakthrough in recognition tasks.