Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio
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
This course includes:
- 6 hours on-demand video
- 4 articles
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
- Students will need to install R and RStudio software but we have a separate lecture to help you install the same
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
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 ConceptsThis 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 RIn 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 ConceptsIn 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.
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