Deep Learning with R [Video]
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 tutorial 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.Style and Approach
This video lecture series simplifies otherwise incredibly dense topics with clear, concise explanations and reproducible, hands-on examples. No prior knowledge of deep learning is assumed, but learners gain intermediate proficiency by the end of the course.
|Course Length||4 hours 4 minutes|
|Date Of Publication||27 Mar 2017|
|Restricted Boltzmann Machines and Deep Belief Networks|
|Reinforcement Learning with ANNs|
|Use-Case – Anomaly Detection through Denoising Autoencoders|