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...is a collective term for characteristics that the two institutions share.
·-- PUBLISHED -----·> Packt Publishing
·-- LECTURESHIP ---·> Deep Learning with R
·-- LECTURE DATE --·> 05-2017
03-2017 {Published}
·-- LEVEL ---------·> [ ] Starting {Beginner|Newcomer}
[■] Progressing {Intermediate|Advanced}
·-- PERIOD in HRS--·> 04+ e
·-- SCALE ---------·> 17x50mb
·-- LECTURE LINK --·> https://goo.gl/6OGfRh
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 ANNÆs and RNNÆs. 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.