Get a crash course on the what there is to learn and how to go about learning more. Deep Learning presents a simplified explanation of some of the hottest topics in data science today.
Data science is a multidisciplinary skill that requires proficiency in hardware and software, domain expertise and communication skills, and statistics and modelling. While individually, all of them are scalable fields, there is a growing need for people who master all three. A person who is proficient in even one field can easily excel in the rest.
Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or more depth. These kinds of nets are capable of discovering hidden structures within unlabelled and unstructured data (i.e. images, sound, and text), which constitute a vast majority of data in the world.
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
