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Deep Learning with R
Deep Learning with R
  • Syllabus
  • Schedule
  • Readings
  • Code
  • Classes
Class sessions
  • Day 1: Fundamentals of Deep Learning I
  • Day 2: Fundamentals of Deep Learning II
  • Day 3: Overfitting, regularization, dropout, pretrained models, word embedding
  • Day 4: Convolutional Neural Networks, images
  • Day 5: Generative Adversarial Networks, Autoencoders, Recurrent Neural Networks, LSTM, GRU, sequence learning
  • Final project

Day 2: Fundamentals of Deep Learning II

Materials from class on Tuesday, June 9, 2020

Slides

Neural network fundamentals

Lecture slides in HTML format

Lecture handouts in PDF format

Hello [Deep Learning] World

Brad Boehmke’s lecture for the Deep Learning with Keras and TensorFlow in R Workflow

Lecture slides in HTML format

Last updated on June 9, 2020

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BIOS 691: Deep Learning with R (Summer 2020)
Virginia Commonwealth University    Biostatistics Graduate Program

Dr. Mikhail Dozmorov    mdozmorov@vcu.edu

Monday through Friday    9:00am – 12:00pm
Online

All content licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content 2020 MGD

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