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Introduction to Deep Learning with R

New – Get a hands-on introduction to modeling, training, and optimizing deep learning models in R in this self-paced course.

GK# 7722

Course Overview

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This course will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers. 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 understand the basics of Deep Learning and move on to exploring advanced ANN’s and RNN’s. You will also dive into CNNs and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of deep learning models for scalability, high performance computing and feature engineering.

Schedule

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  • Delivery Format:
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What You'll Learn

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  • Set up the R package H2O to train deep learning models
  • Understand the core concepts behind deep learning models
  • Use autoencoders to identify anomalous data or outliers
  • Predict or classify data automatically using deep neural networks
  • Build generalizable models using regularization to avoid overfitting the training data

Outline

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On-Demand Outline

Getting started with deep learning

Training a Prediction Model

Identifying Anomalous Data

Training Deep Prediction Models

Tuning and Optimizing Models

Applications of Deep Learning

Who Should Attend

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This course caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. No prior exposure to deep learning is required.

Course Delivery

This course is available in the following formats:

On-Demand

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