Deep Learning with R in Motion

Deep Learning with R in Motion

by Rick Scavetta
Released August 2019
Publisher(s): Manning Publications
ISBN: 10000MNLV201801

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Video description

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 Technology

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.



What's Inside
  • 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 Reader
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.

About the Author

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 manning.com in pBook, eBook, and liveBook formats.

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

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

Table of contents

  1. GETTING STARTED
    1. Welcome to the Video Series
    2. What is Deep Learning?
    3. The Landscape of Deep Learning
    4. The Landscape of Machine Learning
    5. The Two Golden Hypotheses
    6. The 4 Types of Machine Learning
  2. MNIST CASE STUDY
    1. Unit Introduction
    2. The MNIST dataset
    3. A first look at a neural network
    4. The 4 steps of Deep Learning, part 1
    5. The 4 steps of Deep Learning, part 2
    6. The Uses of Derivatives
    7. From Derivatives to Gradients
    8. Momentum in Mini-batch Stochastic Gradient Descent
    9. The 4 steps of Deep Learning, part 3
    10. Basic Model Evaluation
  3. THREE CASE STUDIES FOR DEEP LEARNING
    1. Unit Introduction
    2. The story so far
    3. The Reuters Newswire dataset: data preparation
    4. The Reuters Newswire dataset: model definition and evaluation
    5. The Reuters Newswire dataset: reanalysis
    6. The IMDB Dataset: Data preparation, model definition, and evaluation
    7. The IMDB Dataset: reanalysis
    8. The Boston Housing Dataset: data preparation and model definition
    9. The Boston Housing Dataset: K-fold cross validation and evaluation
    10. Summary of the case studies
  4. MODEL EVALUATION AND THE UNIVERSAL WORKFLOW
    1. Review of the landscape
    2. Validation: 3 varieties
    3. Model Evaluation
    4. Data Pre-processing
    5. The machine learning universal workflow and Part 1 wrap-up
  5. COMPUTER VISION
    1. Unit Intro
    2. Intro to Computer Vision
    3. Convnets on MNIST
    4. Convnets 1: Define Convnets from Scratch
    5. Convnets 1: Import, Compile, and Train
    6. Convnets 2: Data Augmentation
    7. Convnets 3: Pre-Trained Intro
    8. Convnets 3: Pre-Trained Code
  6. TEXT AND SENTENCES
    1. Introduction to Text and Sequences
    2. Word Embeddings from Scratch
    3. Pre-Trained Word Embeddings
    4. RNNs on the IMDb Dataset
    5. LSTMs on the IMDb Dataset
  7. BEST PRACTICES CONCLUSION ON PATTERN MATCHING
    1. Chapter Intro
    2. Idiosyncratic Structures
    3. Callbacks and TensorBoard
    4. A Review of Best Practices

Product information

  • Title: Deep Learning with R in Motion
  • Author(s): Rick Scavetta
  • Release date: August 2019
  • Publisher(s): Manning Publications
  • ISBN: 10000MNLV201801