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What you will be taught


  • Get a strong understanding of Convolutional Neural Networks (CNN) and Deep Learning
  • Build an end-to-end Image recognition mission in R
  • Learn utilization of Keras and Tensorflow libraries
  • Use Artificial Neural Networks (ANN) to make predictions
  • Use Pandas DataFrames to govern knowledge and make statistical computations.
What is roofed in this course?

This course teaches you all of the steps of making a Neural community primarily based mannequin i.e. a Deep Learning mannequin, to resolve enterprise issues.

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 half will get you began with R.

This part will provide help to arrange the R and R studio in your system and it will train you easy methods to carry out some fundamental operations in R.

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

This half will provide you with a strong understanding of ideas concerned in Neural Networks.

In this part you’ll be taught concerning the single cells or Perceptrons and how Perceptrons are stacked to create a community structure. Once structure is about, we perceive the Gradient descent algorithm to search out the minima of a operate and find out how that is used to optimize our community mannequin.

Part 3 (Section 7-11) – Creating ANN mannequin in R

In this half you’ll discover ways to create ANN fashions in R.

We will begin this part by creating an ANN mannequin utilizing Sequential API to resolve a classification downside. We discover ways to outline community structure, configure the mannequin and practice the mannequin. Then we consider the efficiency of our skilled mannequin and use it to foretell on new knowledge. Lastly we discover ways to save and restore fashions.

We additionally perceive the significance of libraries resembling Keras and TensorFlow in this half.

Part 4 (Section 12) – CNN Theoretical Concepts

In this half you’ll find out about convolutional and pooling layers that are the constructing blocks of CNN fashions.

In this part, we are going to begin with the essential concept of convolutional layer, stride, filters and function maps. We additionally clarify how gray-scale photographs are totally different from coloured photographs. Lastly we talk about pooling layer which convey computational effectivity in our mannequin.

Part 5 (Section 13-14) – Creating CNN mannequin in R
In this half you’ll discover ways to create CNN fashions in R.

We will take the identical downside of recognizing vogue objects and apply CNN mannequin to it. We will examine the efficiency of our CNN mannequin with our ANN mannequin and discover that the accuracy will increase by 9-10% once we use CNN. However, this isn’t the top of it. We can additional enhance accuracy by utilizing sure methods which we discover in the subsequent half.

Part 6 (Section 15-18) – End-to-End Image Recognition mission in R
In this part we construct an entire picture recognition mission on coloured photographs.

We take a Kaggle picture recognition competitors and construct CNN mannequin to resolve it. With a easy mannequin we obtain almost 70% accuracy on check set. Then we be taught ideas like Data Augmentation and Transfer Learning which assist us enhance accuracy stage from 70% to almost 97% (nearly as good because the winners of that competitors).

By the top of this course, your confidence in making a Convolutional Neural Network mannequin in R will soar. You’ll have an intensive understanding of easy methods to use CNN to create predictive fashions and resolve picture recognition issues.

Go forward and click on the enroll button, and I’ll see you in lesson 1!


Start-Tech Academy


Below are some common FAQs of scholars who wish to begin their Deep studying journey-

Why use R for Deep Learning?

Understanding R is among the priceless expertise wanted for a profession in Machine Learning. Below are some the explanation why it is best to be taught Deep studying in R

1. It’s a well-liked language for Machine Learning at high tech companies. Almost all of them rent knowledge scientists who use R. Facebook, for instance, makes use of R to do behavioral evaluation with consumer publish knowledge. Google makes use of R to evaluate advert effectiveness and make financial forecasts. And by the best way, it’s not simply tech companies: R is in use at evaluation and consulting companies, banks and different monetary establishments, tutorial establishments and analysis labs, and just about in all places else knowledge wants analyzing and visualizing.

2. Learning the information science fundamentals is arguably simpler in R. R has an enormous benefit: it was designed particularly with knowledge manipulation and evaluation in thoughts.

3. Amazing packages that make your life simpler. Because R was designed with statistical evaluation in thoughts, it has a unbelievable ecosystem of packages and different assets which might be nice for knowledge science.

4. Robust, rising group of information scientists and statisticians. As the sector of information science has exploded, R has exploded with it, changing into one of many fastest-growing languages in the world (as measured by StackOverflow). That means it’s simple to search out solutions to questions and group steerage as you’re employed your approach by means of initiatives in R.

5. Put one other instrument in your toolkit. No one language goes to be the suitable instrument for each job. Adding R to your repertoire will make some initiatives simpler – and in fact, it’ll additionally make you a extra versatile and marketable worker once you’re wanting for jobs in knowledge science.

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

Put merely, machine studying and knowledge mining use the identical algorithms and methods as knowledge mining, besides the sorts of predictions differ. While knowledge mining discovers beforehand unknown patterns and data, machine studying reproduces recognized patterns and data—and additional routinely applies that data to knowledge, decision-making, and actions.

Deep studying, then again, makes use of superior computing energy and particular forms of neural networks and applies them to giant quantities of information to be taught, perceive, and determine sophisticated patterns. Automatic language translation and medical diagnoses are examples of deep studying.