Posted on 2020-01-20, by nokia241186.
Practical Neural Networks and Deep Learning in R
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 50m | 7.56 GB
Instructor: Minerva Singh
Learn the implementation of both ANN & DNN using the H2O package Of R programming language
Discover the powerful R-based deep learning packages such as H2O and MXNET
You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and recurrent neural networks (RNN)
Apply these frameworks to real-life data including credit card fraud data, tumour data, images among others for classification and regression applications
What You Will Learn
Harness the power of R for practical Data Science
Read in data into the R environment from different sources & carry out basic pre-processing tasks
Master the theory of Artificial Neural Networks (ANN)
Implement ANN for classification & regression problems in R
Discover the ability to implement Deep Learning in R
Harness and use the powerful H2O package
Implement both ANN & DNN using the H2O package of R programming language
This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. You will dig deep into the data science features of R that will give you a one-of-a-kind grounding in data science.
You will go all the way from carrying out data reading & cleaning to finally implementing powerful Neural Networks and Deep Learning algorithms and evaluating their performance using R. With this course, you'll have the keys to the entire R Neural Networks and Deep Learning kingdom!
You'll start by absorbing the most valuable R Data Science basics and techniques. Discover easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. You will see how to implement the methods using real data obtained from different sources.
After taking this course, you'll be able to easily use data science packages like the caret, H2O, mxnet to work with real data in R. You'll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
All the codes and supporting files for this course are available at -
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- Ebooks list page : 42520
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