Deep Learning with R Training

Deep Learning with R primarily tends to work with the Data Representation in Artificial Intelligence based Applications.

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Duration: 3 Day

Course fee:$599.00 (₹0)

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Product Description

Deep Learning with R Training primarily aims for understanding the developers with powerful Keras Library and its R Language interface. Deep Learning system generally deals with enabling previously impossible smart application, revolutionizing image recognition and also natural language processing. R generally works with Data Representation used specifically in the Applications. Customer Relationship is better understood with this training as the customer must be easily maintained for the application.  R using Deep Learning involves the process of working with Text creation and dealing with its corresponding sequences for the complete application. Keras Library is used for dealing with Deep Learning Application using Keras Functional API and TensorBoard Visualization Tool. On the completion of the training, the aspirants can gain expertise in creation Deep Learning Application using R Technology in real world scenario.

Objectives

  • Understanding the basic understanding of R Language
  • Understanding the setup of own deep-learning environment
  • Using Image Classification and generation

Advantages

  • Expertise in developing and deploying Machine Learning Application
  • Performance Enhancement in Blockchain Application using R
  • Data Management Capabilities enforcement in Blockchain Application

 

Additional Information

Course Content

1. Basic Fundamentals of Deep Learning

– Introduction to Deep Learning
– Understanding the mathematical building blocks of Neural Networks
– Overview of Neural Networks
– Basic understanding on Machine Learning

2. Basic overview of Deep Learning Algorithms

– Introduction to Forward Propagation Algorithms
– Dealing with Activation Functions
– Concept of Gradient Descent
– Concept of BackPropagation

3. Deep Learning in Practice

– Basics of Convolutional Neural Networks (convnets)
– Dealing with Data Augmentation for Mitigation overfitting
– Working with pretrained convnet for featuring extraction
– Concept of Fine Tuning Pre trained convnet

4. Understanding the basic workflow for Keras in R

5. DL for Text and Sequences

– Dealing with Preprocessing text data into meaningful representation
– Working with Recurrent Neural Networks
– Using 1D convnet for sequence Processing

6. Concept of Advanced Deep Learning best Practices

– Understanding Keras Functionality API
– Working with Keras callbacks
– Dealing with TensorBoard visualization Tool

7. Working with R Interfaces to Keras

8. Practical Implementation of Deep Learning with R on Azure with Keras

9. Concept of Generative Deep Learning

– Using Text Generation with LSTM
– Implementation of DeepDream
– Neural Style Transfer Performing Steps
– Working with Variational autoencoders
– Understanding generative adversarial networks

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