Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.
This course will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN’s and RNN’s. You will deep dive into Convolutional Neural Networks and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.
Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios.
Learn the basics of Deep Learning and Artificial Neural Networks
Understand classification and probabilistic predictions with Single-hidden-layer Neural Networks
Increase your expertise by covering intermediate and advanced Artificial and Recurrent Neural Networks
Get to grips with Convolutional and Deep Belief Networks
Learn practical applications of Deep Learning
Learn about Feature Engineering and Multicore/Cluster Computing
Familiarity with the theoretical underpinnings of neutral networks is highly useful, this course is appropriate for anyone with prior experience in R and a general familiarity with predictive models.
Who is this course intended for?
This course is for anyone with an interest in creating cutting-edge deep learning models in R.