Deep Learning in R


Deep Learning in R

Training description

Machine Learning changes the business reality rapidly. Deep Learning became the main driver of this revolution. Thanks to Deep Learning, such things as speech recognition or computer vision become not only possible but also extremely effective.

“Deep Learning in R” is an advanced training dedicated to everyone who would like to learn how to build powerful models in R powered by Deep Neural Networks.

Duration: 4 days 8 hours each (including an hour lunch break)

Requirements: knowledge of R programming language at an intermediate level (especially in data processing field).

Training agenda

Part one: Let’s get it started!

  • What’s Deep Learning?
  • Machine Learning vs. Deep Learning
  • Algorithms overview

Part two: The necessary foundation

  • Introduction to Neural network
  • Types of activation functions
  • Backpropagation method
  • Gradient Descent vs. Stochastic Gradient Descent
  • Overfitting
  • Model validation

Part three: Write it by yourself!

  • Building deep neural networks
  • Building models by Keras

Part four: Let’s tensor it!

  • Introduction to Tensorflow
  • Building graphs by using Tensorflow
  • How to visualise graph by using Tensorboard

Part five: An infinity of possibilities

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • How to build neural network for structured data – Entity Embeddings

Part six: In search of best solution…

  • Regularization
  • Hyperparameter tuning

Contact us about closed training

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