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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience, intermediate R programming, and basic to intermediate IT skills

Who should attend & recommended skills

  • This course is geared for Python developers, analysts or others who wish to use the powerful Keras library and its R language interface and get concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life.
  • Skill-level: Foundation-level Deep Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • R programming skills: Intermediate (3-5 years’ experience) required
  • Machine Learning: Not required
  • Deep Learning: Not required

About this course

Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher Franois Chollet and adapted for R by RStudio founder J. J. Allaire, this course builds your understanding of deep learning through intuitive explanations and practical examples. You’ll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.

Skills acquired & topics covered

  • Using the powerful Keras library and its R language interface
  • Building your understanding of deep learning through intuitive explanations and practical examples
  • Deep learning from first principles
  • Setting up your own deep-learning environment
  • Image classification and generation
  • Deep learning for text and sequences

Course breakdown / modules

  • Artificial intelligence, machine learning, and deep learning
  • Before deep learning: a brief history of machine learning
  • Why deep learning? Why now?

  • A first look at a neural networkData representations for neural networks
  • The gears of neural networks: tensor operations
  • The engine of neural networks: gradient-based optimization
  • Looking back at our first example

  • Anatomy of a neural network
  • Introduction to Keras
  • Setting up a deep-learning workstation 
  • Classifying movie reviews: a binary classification example
  • Classifying newswires: a multiclass classification example
  • Predicting house prices: a regression example

  • Four branches of machine learning
  • Evaluating machine-learning models
  • Data preprocessing, feature engineering, and feature learning
  • Overfitting and underfitting
  • The universal workflow of machine learning

  • Introduction to convnets
  • Training a convnet from scratch on a small dataset
  • Using a pretrained convnet
  • Visualizing what convnets learn

  • Working with text data
  • Understanding recurrent neural networks
  • Advanced use of recurrent neural networks
  • Sequence processing with convnets

  • Going beyond the sequential model: the Keras functional API
  • Inspecting and monitoring deep-learning models using Keras callba-acks and TensorBoard
  • Getting the most out of your models

  • Text generation with LSTM
  • DeepDream
  • Neural style transfer
  • Generating images with variational autoencoders
  • Introduction to generative adversarial networks

  • Key concepts in review
  • The limitations of deep learning
  • The future of deep learning
  • Staying up to date in a fast-moving field
  • Final words