Computer Science, Machine Learning and Big Data

Deep learning is an emerging area in machine learning. It is based entirely on the understanding of neural networks and their different architectures to achieve specific goals. Convolutional neural networks are used in computer vision to help machines identify faces, animals, cars, etc. Different convolutional neural network architectures have been developed in recent times to make computer vision tasks easier. Transfer learning can be applied to use the weights and architecture from an already trained network like VGG16 or VGG19 and apply it to relevant tasks. Recurrent neural networks are another class of neural networks that allows context to be preserved and hence is used for language processing. There are various types such as LSTMs, GRUs. Each has their advantages. This study discusses the basics of neural networks, mathematics of back-propagations, intricacies of CNNs, RNNs and then advance architectures like residual networks and ladder networks.