**Deep Learning with R in Motion**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 52m | 3.71 GB

eLearning | Skill level: All Levels

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!

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!

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.

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

**+ Table of Contents**

01 Welcome to the Video Series

02 What is Deep Learning

03 The Landscape of Deep Learning

04 The Landscape of Machine Learning

05 The Two Golden Hypotheses

06 The 4 Types of Machine Learning

07 Unit Introduction

08 The MNIST dataset

09 A first look at a neural network

10 The 4 steps of Deep Learning, part 1

11 The 4 steps of Deep Learning, part 2

12 The Uses of Derivatives

13 From Derivatives to Gradients

14 Momentum in Mini-batch Stochastic Gradient Descent

15 The 4 steps of Deep Learning, part 3

16 Basic Model Evaluation

17 Unit Introduction

18 The story so far

19 The Reuters Newswire dataset – data preparation

20 The Reuters Newswire dataset – model definition and evaluation

21 The Reuters Newswire dataset – reanalysis

22 The IMDB Dataset – Data preparation, model definition, and evaluation

23 The IMDB Dataset – reanalysis

24 The Boston Housing Dataset – data preparation and model definition

25 The Boston Housing Dataset – K-fold cross validation and evaluation

26 Summary of the case studies

27 Review of the landscape

28 Validation – 3 varieties

29 Model Evaluation

30 Data Pre-processing

31 The machine learning universal workflow and Part 1 wrap-up

32 Unit Intro

33 Intro to Computer Vision

34 Convnets on MNIST

35 Convnets 1 – Define Convnets from Scratch

36 Convnets 1 – Import, Compile, and Train

37 Convnets 2 – Data Augmentation

38 Convnets 3 – Pre-Trained Intro

39 Convnets 3 – Pre-Trained Code

40 Introduction to Text and Sequences

41 Word Embeddings from Scratch

42 Pre-Trained Word Embeddings

43 RNNs on the IMDb Dataset

44 LSTMs on the IMDb Dataset

45 Chapter Intro

46 Idiosyncratic Structures

47 Callbacks and TensorBoard

48 A Review of Best Practices

Resolve the captcha to access the links!