About This Course

Deep Learning is the most exciting sub-field of machine learning. Many state of the art results in computer vision are obtained using a Deep Neural Network.

In this course, we will start with a theoretical understanding of simple neural nets and gradually move to Deep Neural Nets and Convolutional Neural Networks.

Not only will we go over Deep Net architectures used for solving various computer vision problems, we will also go over practical considerations needed to successfully train Deep Neural Networks. These include how to prepare datasets, how to perform sanity checks before embarking on training that can take hours, how to use visualization tools to debug the training process, what workflows to use when the results are not satisfactory, and finally how to deploy your network on the cloud.