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