Data Science


Image Recognition for Beginners using CNN in R Studio

Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio

4.55 (193 reviews)


6.5 hours


Jun 2021

Last Update
Regular Price

What you will learn

Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning

Build an end-to-end Image recognition project in R

Learn usage of Keras and Tensorflow libraries

Use Artificial Neural Networks (ANN) to make predictions


You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Recognition model in R, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

  • Identify the Image Recognition problems which can be solved using CNN Models.

  • Create CNN models in R using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

  • Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 (Section 2)- Setting up R and R Studio with R crash course

    • This part gets you started with R.

      This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.

  • Part 2 (Section 3-6) - ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 (Section 7-11) - Creating ANN model in R

    In this part you will learn how to create ANN models in R.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 (Section 12) - CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Part 5 (Section 13-14) - Creating CNN model in R
    In this part you will learn how to create CNN models in R.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Part 6 (Section 15-18) - End-to-End Image Recognition project in R
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Below are some popular FAQs of students who want to start their Deep learning journey-

Why use R for Deep Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.


Image Recognition for Beginners using CNN in R Studio
Image Recognition for Beginners using CNN in R Studio
Image Recognition for Beginners using CNN in R Studio
Image Recognition for Beginners using CNN in R Studio




Course resources

Setting Up R Studio and R crash course

Installing R and R studio

Basics of R and R studio

Packages in R

Inputting data part 1: Inbuilt datasets of R

Inputting data part 2: Manual data entry

Inputting data part 3: Importing from CSV or Text files

Creating Barplots in R

Creating Histograms in R

Single Cells - Perceptron and Sigmoid Neuron


Activation Functions

Neural Networks - Stacking cells to create network

Basic Terminologies

Gradient Descent

Back Propagation

Important concepts: Common Interview questions

Some Important Concepts

Standard Model Parameters


Tensorflow and Keras

Keras and Tensorflow

Installing Keras and Tensorflow

R - Dataset for classification problem

Data Normalization and Test-Train Split

R - Building and training the Model

Building, Compiling and Training

Evaluating and Predicting

The NeuralNets Package

ANN with NeuralNets Package

Saving and Restoring Models

Saving - Restoring Models and Using Callbacks

Hyperparameter Tuning

Hyperparameter Tuning

CNN - Basics

CNN Introduction



Filters and Feature maps



Creating CNN model in R

CNN on MNIST Fashion Dataset - Model Architecture

Data Preprocessing

Creating Model Architecture

Compiling and training

Model Performance

Analyzing impact of Pooling layer

Comparison - Pooling vs Without Pooling in R

Project : Creating CNN model from scratch

Project - Introduction

Data for the project

Project in R - Data Preprocessing

CNN Project in R - Structure and Compile

Project in R - Training

Project in R - Model Performance

Project : Data Augmentation for avoiding overfitting

Project in R - Data Augmentation

Project in R - Validation Performance

Transfer Learning : Basics





Transfer Learning

Transfer Learning in R

Project - Transfer Learning - VGG16 (Implementation)

Project - Transfer Learning - VGG16 (Performance)


Kanagala8 March 2021

It's a course with detailed explaination from the scratch level and I love the examples that the instructor is giving to clarify the hard topics.

Erika2 November 2020

Lo fue, ha sido muy interesante, aunque al no contar con un equipo con buenas características es difícil hacer los mismo que el curso o seguirles bien el paso :(

Edgar5 August 2020

Muy bueno, sigo con el problema de resolver casos de la vida real, no solo concursos para mostrar habilidades, sino para responder a necesidades reales...

Luis3 July 2020

I have enjoyed this webinar to use R with Keras, and to review CNN image classification . Many thanks.


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