Applied Deep Learning: Unsupervised Data

This 11-video course explores the concept of deep learning and implementation of deep learning-based frameworks for natural language processing (NLP) and audio data analysis. Discover the architectures of recurrent neural network (RNN) that can be used in modeling NLP, and the challenges of unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature machine learning. First, examine the prominent statistical classification models and compare generative classifiers with discriminative classifiers; then recall different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model. Learn about setting up and working with PixelCNN; explore differences between multilayer perception (MLP), convolutional neural network (CNN), and RNN. Explore the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. Finally, take a look at encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. The concluding exercise involves recalling RNN architecture that can be used in modeling NLP, variants of ResNet, and setting up PixelCNN.