Deep Learning With Keras And Tensorflow In R

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

What you'll learn
Basic knowledge about convolutional neural netowrks
How to train a CNN to make predictions
Image recognition (for example, human face recognition)
Character recognition
Requirements
Knowledge of R programming
Basic knowledge of data analysis with R
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!

Overview

Section 1: Getting Started

Lecture 1 Introduction

Section 2: Basic Notions

Lecture 2 What Are Convolutional Neural Networks?

Lecture 3 Online Articles on the Topic

Lecture 4 Tools of the Trade

Lecture 5 Video Tutorials

Section 3: Building Classification Models with CNNS

Lecture 6 Classification Problem (Binomial Response): Data Preparation

Lecture 7 Classification Problem (Binomial Response): Building the Model

Lecture 8 Classification Problem (Binomial Response): Making Predictions

Lecture 9 Classification Problem (Multinomial Response): Data Preparation

Lecture 10 Classification Problem (Multinomial Response): Building the Model

Lecture 11 Classification Problem (Multinomial Response): Making Predictions

Section 4: Recognizing Human Faces From Trees

Lecture 12 Data Preparation

Lecture 13 Creating the Training Set and the Test Set

Lecture 14 Building the Model

Lecture 15 Making Predictions in the Test Set

Lecture 16 Making Predictions on New Data

Section 5: Recognizing Animals

Lecture 17 Recognizing Bears From Foxes: Data Preparation

Lecture 18 Recognizing Bears From Foxes: Training Set and Test Set

Lecture 19 Recognizing Bears From Foxes: Building the Model

Lecture 20 Recognizing Bears From Foxes: Making Predictions

Lecture 21 Recognizing Bears From Foxes: Making Predictions on New Data

Lecture 22 Recognizing Bears, Foxes and Mice: Data Preparation

Lecture 23 Recognizing Bears, Foxes and Mice: Training Set and Test Set

Lecture 24 Recognizing Bears, Foxes and Mice: Building the Model

Lecture 25 Recognizing Bears, Foxes and Mice: Making Predictions

Lecture 26 Recognizing Bears, Foxes and Mice: Making Predictions on New Data

Section 6: Telling Asterisks From Hashtags

Lecture 27 Data Preparation

Lecture 28 Training Set and Test Set

Lecture 29 Building the Model

Lecture 30 Making Predictions

Section 7: Recognizing Hand-Written Numbers

Lecture 31 Data Preparation

Lecture 32 Model Building

Lecture 33 Making Predictions

Lecture 34 Making Predictions on New Data

Section 8: Practice

Lecture 35 Data Sets Descriptions

Lecture 36 Practical Exercises

Section 9: Useful Links

Lecture 37 Download Your Resources Here

Intermediate or beginner R users who want to learn deep learning,Wannabe data scientists

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