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Deep Learning with R

  • Course Code: Data Science - Deep Learning with R
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to use the powerful Keras library and its R language interface

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Deep Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to use the powerful Keras library and its R language interface 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 François 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. 

Working in a hands-on learning environment, led by our Deep Learning with R expert instructor, students will learn about and explore: 

  • use the powerful Keras library and its R language interface.  
  • builds your understanding of deep learning through intuitive explanations and practical examples. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Deep learning from first principles 
  • Setting up your own deep-learning environment 
  • Image classification and generation 
  • Deep learning for text and sequences 

Audience & Pre-Requisites 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • You’ll need intermediate R programming skills.  
  • No previous experience with machine learning or deep learning is assumed. 

Course Agenda / Topics 

  1. What is deep learning?free 
  • Artificial intelligence, machine learning, and deep learning 
  • Before deep learning: a brief history of machine learning 
  • Why deep learning? Why now? 
  1. Before we begin: the mathematical building blocks of neural networksfree 
  • A first look at a neural network 
  • Data 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 
  1. Getting started with neural networksfree 
  • 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 
  1. Fundamentals of machine learning 
  • 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 
  1. Deep learning for computer vision 
  • Introduction to convnets 
  • Training a convnet from scratch on a small dataset 
  • Using a pretrained convnet 
  • Visualizing what convnets learn 
  1. Deep learning for text and sequences 
  • Working with text data 
  • Understanding recurrent neural networks 
  • Advanced use of recurrent neural networks 
  • Sequence processing with convnets 
  1. Advanced deep-learning best practices 
  • 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 
  1. Generative deep learning 
  • Text generation with LSTM 
  • DeepDream 
  • Neural style transfer 
  • Generating images with variational autoencoders 
  • Introduction to generative adversarial networks 
  1. Conclusions 
  • 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 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

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