**Requirements**

- Students will need to install R and RStudio software but we have a separate lecture to help you install the same

**Description**

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!

Cheers

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.

**Who this course is
for:**

- People pursuing a career in data science
- Working Professionals beginning their Deep Learning journey
- Anyone curious to master image recognition from Beginner level in short span of time