Deep Learning with R in Motion
Rick J. Scavetta
  • Course duration: 3h 52m

The videos are great: the contents, their didactic perspective, and the technical realisation too!

Anonymous Reviewer

Deep Learning with R in Motion teaches you to apply deep learning to text and images using the powerful Keras library and its R language interface. This liveVideo course builds your understanding of deep learning up through intuitive explanations and fun, hands-on examples!

About the subject

Machine learning has made remarkable progress in recent years. Deep learning systems have revolutionized image recognition, natural-language processing, and other applications for identifying complex patterns in data. The Keras library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep learning tasks!

About the video

See it. Do it. Learn it! The keras package for R brings the power of deep learning to R users. Deep Learning with R in Motion locks in the essentials of deep learning and teaches you the techniques you'll need to start building and using your own neural networks for text and image processing.

Instructor Rick Scavetta takes you through a hands-on ride through the powerful Keras package, a TensorFlow API. You'll start by digging into case studies for how and where to use deep learning. Then, you'll master the essential components of a deep learning neural network as you work hands-on through your first examples. You'll continue by exploring dense and recurrent neural networks, convolutional and generative networks, and how they all work together.

And that's just the beginning! You'll go steadily deeper, making your network more robust and efficient. As your work through each module, you'll train your network and pick up the best practices used by experts like expert instructor Rick Scavetta, Keras library creator and author of Deep Learning in Python François Chollet, and JJ Allaire, founder of RStudio, creator of the R bindings for Keras, and coauthor of Deep Learning in R! You'll beef up your skills as you practice with R-based applications in computer vision, natural-language processing, and generative models, ready for the real-world.

This liveVideo course can be used by itself or with the Manning books Deep Learning with R and Deep Learning with Python. All examples are in R.

Table of Contents detailed table of contents

Getting Started

Welcome to the Video Series

What is Deep Learning?

Extra reading material

The Landscape of Deep Learning

The Landscape of Machine Learning

The Two Golden Hypotheses

The 4 Types of Machine Learning

MNIST Case Study

Unit Introduction

The MNIST dataset

A first look at a neural network

The 4 steps of Deep Learning, part 1

The 4 steps of Deep Learning, part 2

The Uses of Derivatives

From Derivatives to Gradients

Momentum in Mini-batch Stochastic Gradient Descent

The 4 steps of Deep Learning, part 3

Basic Model Evaluation

Three Case Studies for Deep Learning

Unit Introduction

The story so far

The Reuters Newswire dataset: data preparation

The Reuters Newswire dataset: model definition and evaluation

The Reuters Newswire dataset: reanalysis

The IMDB Dataset: Data preparation, model definition, and evaluation

The IMDB Dataset: reanalysis

Classification Exercises

The Boston Housing Dataset: data preparation and model definition

The Boston Housing Dataset: K-fold cross validation and evaluation

Regression exercises

Hyperparameter tuning with caret

Summary of the case studies

Model evaluation and the universal workflow

Review of the landscape

Validation: 3 varieties

Model Evaluation

Supplementary reading: L1 and L2 Regularization

Data Pre-processing

Supplementary reading: Revisiting MNIST

The machine learning universal workflow and Part 1 wrap-up

Computer Vision

Unit Intro

Intro to Computer Vision

Convnets on MNIST

Convnets 1: Define Convnets from Scratch

Convnets 1: Import, Compile, and Train

Convolution in Depth

Convnets 2: Data Augmentation

Convnets 3: Pre-Trained Intro

Convnets 3: Pre-Trained Code

Text & Sequences

Introduction to Text and Sequences

Word Embeddings from Scratch

Pre-Trained Word Embeddings

RNNs on the IMDb Dataset

LSTMs on the IMDb Dataset

Best Practices & Conclusion on Pattern Matching

Chapter Intro

Idiosyncratic Structures

Callbacks and TensorBoard

A Review of Best Practices

Course wrap up and additional resources



You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.

What you will learn

  • The 4 steps of Deep Learning
  • Using R with Keras and TensorFlow
  • Working with the Universal Workflow
  • Computer vision with R
  • Recurrent neural networks
  • Everyday best practices
  • Generative deep learning

About the instructor

Rick Scavetta is a biologist, workshop trainer, freelance data scientist, cofounder of Science Craft, and founder of Scavetta Academy, companies dedicated to helping scientists better understand and visualize their data. Rick's practical, hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.

Deep Learning with Python by François Chollet and Deep Learning with R adapted by J.J. Allaire are both available at in pBook, eBook, and liveBook formats.

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A great intro for someone with a solid understanding of programming and a hazy understanding of math and statistics, much better than most.

Anonymous Reviewer

I love the richness of animations and infographics. The required knowledge (intermediate R skills) are just right for me too!

Anonymous Reviewer