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UDEMY [100% OFF COUPON - Time left : 1 day] Deep Learning with Keras and Tensorflow in R

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Deep Learning with Keras and Tensorflow in R
Author : Bogdan Anastasiei
Last update : 12/2020
Language : English​
What you'll learn
  • Basic knowledge about convolutional neural netowrks
  • How to train a CNN to make predictions
  • Image recognition (for example, human face recognition)
  • Character recognition
Description
In this course you will learn how to build powerful convolutional neural networks in R, from scratch. This special kind of deep networks is used to make accurate predictions in various fields of research, either academic or practical.
If you want to use R for advanced tasks like image recognition, face detection or handwriting recognition, this course is the best place to start. It’s a hands-on approach on deep learning in R using convolutional neural networks. All the procedures are explained live, step by step, in every detail.
Most important, you will be able to apply immediately what you will learn, by simply replicating and adapting the code we will be using in the course.
To build and train convolutional neural networks, the R program uses the capabilities of the Python software. But don’t worry if you don’t know Python, you won’t have to use it! All the analyses will be performed in the R environment. I will tell you exactly what to do so you can call the Python functions from R and create convolutional neural networks.
Now let’s take a look at what we’ll cover in this course.
The opening section is meant to provide you with a basic knowledge of convolutional neural networks. We’ll talk about the architecture and functioning of these networks in an accessible way, without getting into cumbersome mathematical aspects. Next, I will give you exact instructions concerning the technical requirements for running the Python commands in R.
The main sections of the course are dedicated to building, training and evaluating convolutional neural networks.
We’ll start with two simple prediction problems where the input variable is numeric. These problems will help us get familiar with the process of creating convolutional neural networks.
Afterwards we’ll go to some real advanced prediction situations, where the input variables are images. Specifically, we will learn to:
  • recognize a human face (distinguish it from a tree – or any other object for that matter)
  • recognize wild animal images (we’ll use images with bears, foxes and mice)
  • recognize special characters (distinguish an asterisk from a hashtag)
  • recognize and classify handwritten numbers.
At the end of the course you’ll be able to apply your knowledge in many image classification problems that you could meet in real life. The practical exercises included in the last section will hopefully help you strengthen you abilities.
This course is your opportunity to make the first steps in a fascinating field – image recognition and classification. It is a complex and demanding field, but don’t let that scare you. I have tried to make everything as easy as possible.
So click the “Enroll” button to get instant access. You will surely acquire some invaluable skills.
See you on the other side!
Who this course is for:
  • Intermediate or beginner R users who want to learn deep learning
  • Wannabe data scientists
Course content
9 sections • 37 lectures • 3h 32m total lengthExpand all sections
Getting Started : 1 lecture • 4min
  • Introduction
Basic Notions : 4 lectures • 8min
  • What Are Convolutional Neural Networks?
  • Online Articles on the Topic
  • Tools of the Trade
  • Video Tutorials
Building Classification Models with CNNS : 6 lectures • 55min
  • Classification Problem (Binomial Response): Data Preparation
  • Classification Problem (Binomial Response): Building the Model
  • Classification Problem (Binomial Response): Making Predictions
  • Classification Problem (Multinomial Response): Data Preparation
  • Classification Problem (Multinomial Response): Building the Model
  • Classification Problem (Multinomial Response): Making Predictions
Recognizing Human Faces From Trees : 5 lectures • 38min
  • Data Preparation
  • Creating the Training Set and the Test Set
  • Building the Model
  • Making Predictions in the Test Set
  • Making Predictions on New Data
Recognizing Animals : 10 lectures • 57min
  • Recognizing Bears From Foxes: Data Preparation
  • Recognizing Bears From Foxes: Training Set and Test Set
  • Recognizing Bears From Foxes: Building the Model
  • Recognizing Bears From Foxes: Making Predictions
  • Recognizing Bears From Foxes: Making Predictions on New Data
  • Recognizing Bears, Foxes and Mice: Data Preparation
  • Recognizing Bears, Foxes and Mice: Training Set and Test Set
  • Recognizing Bears, Foxes and Mice: Building the Model
  • Recognizing Bears, Foxes and Mice: Making Predictions
  • Recognizing Bears, Foxes and Mice: Making Predictions on New Data
Telling Asterisks From Hashtags : 4 lectures • 14min
  • Data Preparation
  • Training Set and Test Set
  • Building the Model
  • Making Predictions
Recognizing Hand-Written Numbers : 4 lectures • 36min
  • Data Preparation
  • Model Building
  • Making Predictions
  • Making Predictions on New Data
Practice : 2 lectures • 1min
  • Data Sets Descriptions
  • Practical Exercises
Useful Links : 1 lecture • 1min
  • Download Your Resources Here
Instructor
Bogdan AnastasieiUniversity Teacher and Consultant
  • 4.4 Instructor Rating
  • 6,373 Reviews
  • 286,872 Students
  • 12 Courses
My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting.