Deep Learning with R Training


Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn’t beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. This course, Deep Learning with R, introduces the world of deep learning using the powerful Keras library and its R language interface.

Course Objectives:

  • What is deep learning?
  • Install R keras package
  • Getting started with neural networks
  • Deep learning for computer vision
  • Deep learning for text and sequences


7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Getting Started on R Keras
    1. What is Deep Learning?
    2. What is Keras
    3. Why Using R Keras Package
    4. Install R Keras Package
  2. Neural Networks
    1. What is Neural Network
    2. Activation Functions
    3. Neural Network on MNIST
  3. Convolutional Neural Network
    1. What is CNN?
    2. CNN Architecture
    3. Convolution Layers
    4. Pooling and Dropout Layers
    5. CNN on MNIST dataset
  4. Transfer Learning
    1. What is Transfer Learning
    2. Pre-Trained Image Recognition Models
    3. Fine Tuning Pre-Trained Models
  5. Recurrent Neural Network
    1. Sequential Data
    2. What is RNN?
    3. Types of RNN
    4. How to train a RNN
    5. Long Term Dependencies
    6. LSTM and GRU Cells
    7. RNN on IMDB datasets