Deep Learning Essentials is a 3-day course designed to provide a comprehensive
understanding of the fundamentals of deep learning. The course covers common deep learning architectures for supervised and unsupervised learning. Starting from a brief description of gradient-based learning algorithms, several common deep architectures are going to be covered, including: multi-layer perceptrons, deep autoencoders, convolutional neural networks, recurrent neural networks and generative adversarial networks. Modern derivatives of such architectures, along with multiple applications utilizing them are going to be discussed.
The participants will get familiar with implementing the state-of-the-art deep learning models on open source deep learning technology, primarily Tensorflow and Keras.