Deep Learning with Keras and Tensorflow in R

Learn to use convolutional neural networks for image recognition, character recognition and accurate predictions.

4.25 (17 reviews)
Udemy
platform
English
language
Data & Analytics
category
5,186
students
3.5 hours
content
Dec 2020
last update
$19.99
regular price

What you will learn

Basic knowledge about convolutional neural netowrks

How to train a CNN to make predictions

Image recognition (for example, human face recognition)

Character recognition

Description

In this course you will learn how to build powerful convolutional neural networks in R, from scratch. This special kind of deep networks is used to make accurate predictions in various fields of research, either academic or practical.


If you want to use R for advanced tasks like image recognition, face detection or handwriting recognition, this course is the best place to start. It’s a hands-on approach on deep learning in R using convolutional neural networks. All the procedures are explained live, step by step, in every detail.


Most important, you will be able to apply immediately what you will learn, by simply replicating and adapting the code we will be using in the course.


To build and train convolutional neural networks, the R program uses the capabilities of the Python software. But don’t worry if you don’t know Python, you won’t have to use it! All the analyses will be performed in the R environment. I will tell you exactly what to do so you can call the Python functions from R and create convolutional neural networks.


Now let’s take a look at what we’ll cover in this course.


The opening section is meant to provide you with a basic knowledge of convolutional neural networks. We’ll talk about the architecture and functioning of these networks in an accessible way, without getting into cumbersome mathematical aspects. Next, I will give you exact instructions concerning the technical requirements for running the Python commands in R.


The main sections of the course are dedicated to building, training and evaluating convolutional neural networks.


We’ll start with two simple prediction problems where the input variable is numeric. These problems will help us get familiar with the process of creating convolutional neural networks.


Afterwards we’ll go to some real advanced prediction situations, where the input variables are images. Specifically, we will learn to:


  • recognize a human face (distinguish it from a tree – or any other object for that matter)

  • recognize wild animal images (we’ll use images with bears, foxes and mice)

  • recognize special characters (distinguish an asterisk from a hashtag)

  • recognize and classify handwritten numbers.


At the end of the course you’ll be able to apply your knowledge in many image classification problems that you could meet in real life. The practical exercises included in the last section will hopefully help you strengthen you abilities.


This course is your opportunity to make the first steps in a fascinating field – image recognition and classification. It is a complex and demanding field, but don’t let that scare you. I have tried to make everything as easy as possible.


So click the “Enroll” button to get instant access. You will surely acquire some invaluable skills.


See you on the other side!

Content

Getting Started

Introduction

Basic Notions

What Are Convolutional Neural Networks?
Online Articles on the Topic
Tools of the Trade
Video Tutorials

Building Classification Models with CNNS

Classification Problem (Binomial Response): Data Preparation
Classification Problem (Binomial Response): Building the Model
Classification Problem (Binomial Response): Making Predictions
Classification Problem (Multinomial Response): Data Preparation
Classification Problem (Multinomial Response): Building the Model
Classification Problem (Multinomial Response): Making Predictions

Recognizing Human Faces From Trees

Data Preparation
Creating the Training Set and the Test Set
Building the Model
Making Predictions in the Test Set
Making Predictions on New Data

Recognizing Animals

Recognizing Bears From Foxes: Data Preparation
Recognizing Bears From Foxes: Training Set and Test Set
Recognizing Bears From Foxes: Building the Model
Recognizing Bears From Foxes: Making Predictions
Recognizing Bears From Foxes: Making Predictions on New Data
Recognizing Bears, Foxes and Mice: Data Preparation
Recognizing Bears, Foxes and Mice: Training Set and Test Set
Recognizing Bears, Foxes and Mice: Building the Model
Recognizing Bears, Foxes and Mice: Making Predictions
Recognizing Bears, Foxes and Mice: Making Predictions on New Data

Telling Asterisks From Hashtags

Data Preparation
Training Set and Test Set
Building the Model
Making Predictions

Recognizing Hand-Written Numbers

Data Preparation
Model Building
Making Predictions
Making Predictions on New Data

Practice

Data Sets Descriptions
Practical Exercises

Useful Links

Download Your Resources Here

Screenshots

Deep Learning with Keras and Tensorflow in R - Screenshot_01Deep Learning with Keras and Tensorflow in R - Screenshot_02Deep Learning with Keras and Tensorflow in R - Screenshot_03Deep Learning with Keras and Tensorflow in R - Screenshot_04

Reviews

Dr.
June 6, 2022
Dozent weiss nicht dass in Powerpoint einen Präsentationsmodus gibt. Oberflächlich. Kein Quelltext. Aber wenigstens gute Quellenangaben.
Gulshan
April 27, 2021
A lot of basic theory is not provided. Unlike Krill’s Super data science groups course A to Z of data science.

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3653860
udemy ID
11/23/2020
course created date
12/9/2020
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