Deep Learning Based Convulsive Neural Network (CNN) to identify images using bananas and tensorflow in R Studio
You are looking for the perfect Convulsive Neural Network (CNN) course Which teaches you everything you need to create an image recognition model in R, right?
You have found the right Convoluted Neural Network Course!
After completing this course, Dr. You will be able to:
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Identify image recognition problems that can be solved using CNN models.
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Create CNN models in R using the Keras and Tensorflow libraries and analyze their results.
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Practice, discuss and understand deep learning concepts with confidence
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There is a clear understanding of advanced image recognition models like LeNet, GoogleNet, VGG16 etc.
How will this course help you?
A Verification certificate of completion It is offered to all students taking the Convolutionary Neural Network course.
If you are an analyst or ML scientist, or a student who wants to learn and apply deep learning in real world image recognition issues, this course will give you a solid foundation by teaching you some cutting edge concepts of deep learning. And their implementation in R without much mathematics.
Why should you choose this course?
This course covers all the steps required to create an image recognition model using convoluted neural networks.
Most courses focus only on teaching how to conduct analysis but we believe that having a strong theoretical understanding of concepts enables us to build a better model. And after running the analysis, one should be able to determine how good the model is and actually interpret the results to help the business.
What qualifies us to teach you?
This course is taught by Abhishek and Pukhraj. As a manager at a global analytics consulting firm, we have helped businesses use in-depth learning techniques to solve their business problems, and we have used our experience to incorporate practical aspects of data analysis into this course.
With over 300,000 registrations and thousands of 5-star reviews like this – we are also the creators of some of the most popular online courses:
This is very good, I like that all the explanations given can be understood by the common man – Joshua
Thanks to the author for this wonderful course. You are the best and this course is worth it. – Daisy
Our word
It is our job to teach our students and we are committed to that. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post questions in the course or send us a direct message.
Download practice files, take practice tests and complete assignments
With each lecture, class notes are attached for you to follow. You can also take a practice exam to understand your concept. There is a final practical assignment for you to actually implement your education.
What is included in this course?
This course teaches you all the steps to build a neural network based model i.e. deep learning model to solve business problems.
Following is the content of this course on ANN:
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Part 1 (Section 2) – Setting up R&R Studio with R Crash Course
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This part starts you off with r.
This section will help you set up R and R Studio on your system and teach you how to perform some basic operations in R.
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Part 2 (Section 3-6) – ANN Theoretical Concepts
This section will give you a solid understanding of the concepts involved in neural networks.
In this section you will learn how perceptrons are stacked to create single cell or perceptrons and network architectures. Once the architecture is set up, we understand the gradient descent algorithm to find out the minima of the function and how it is used to optimize our network model.
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Part 3 (Section 7-11) – Creating ANN Model in R
In this section you will learn how to create ANN models in R.
To solve the classification problem we will start this section by creating ANN model using sequential API. We learn how to define a network architecture, how to configure a model, and how to train a model. We then evaluate the performance of our trained model and use it to make predictions on new data. Finally we learn how to save and restore the model.
We also understand the importance of libraries like Kera and Tenserflow in this area.
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Part 4 (Section 12) – CNN Theoretical Concepts
In this section you will learn about convoluted and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic principles of convoluted layers, strides, filters and feature maps. We also explain how gray-scale images differ from color images. Finally we discuss the pooling layer that leads to computer performance in our model.
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Part 5 (Section 13-14) – Creating a CNN Model in R.
In this section you will learn how to create CNN models in R.We will take the same issue of identifying fashion objects and apply the CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases 9-10% when we use CNN. However, this is not the end of the story. We can further improve the accuracy by using some of the techniques we explored in the next section.
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Part 6 (Section 15-18) – End-to-End Image Recognition Project in R.
In this section we create a complete image recognition project on color images.We take a Kaggle image recognition contest and create a CNN model to solve it. With a simple model we get about 70% accuracy on the test set. We then learn concepts like data augmentation and transfer learning which help us improve the accuracy level from 70% to almost 97% (as good as the winners in that competition).
At the end of this course, your confidence to build a convoluted neural network model in R will increase. You will have a thorough knowledge of how to use CNN to create predictive models and solve image recognition problems.
Go ahead and click on the Enrollment button and I’ll see you in Chapter 1!
Cheers
Start-Tech Academy
A
Below are some popular FAQs for students who want to start their in-depth study journey-
Why use R for deep learning?
R Understanding is one of the most valuable skills required for a career in machine learning. Here are some reasons why you should learn Deep Learning in R.
1. It is a popular language for machine learning in top tech companies. Almost all data scientists hire people who use R. Facebook, for example, to use R to analyze behavior with a user’s post data. Uses Google R to evaluate advertising effectiveness and make financial predictions. And by the way, these are not just tech firms: R is used in analysis and consulting firms, banks and other financial institutions, educational institutions and research laboratories, and everywhere else analysis and visualizing of data is required.
2. Learning the basics of data science in R is undoubtedly easy. R has one major advantage: it is specifically designed with data handling 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 is great for data science.
4. A strong, growing community of data scientists and statisticians. As the field of data science has evolved, R has exploded, becoming the fastest growing language in the world (as measured by stackoverflow). This means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Keep another tool in your toolkit. No single language will be the right tool for every task. Adding R to your store will make some projects easier – and of course, it will also make you a more flexible and marketable employee when you are looking for jobs in data science.
What is the difference between data mining, machine learning and deep learning?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, except that the estimates are different. While data mining seeks previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge — and further that information is automatically applied 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 for learning, understanding, and identifying complex patterns. Automatic language translation and medical diagnostics are examples of in-depth learning.
Deep Learning Based Convulsive Neural Network (CNN) to identify images using bananas and tensorflow in R Studio
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