Deep Learning with Keras and Tensorflow in R
Deep Learning with Keras and Tensorflow in R, available at $19.99, has an average rating of 3.85, with 37 lectures, based on 18 reviews, and has 5186 subscribers.
You will learn about Basic knowledge about convolutional neural netowrks How to train a CNN to make predictions Image recognition (for example, human face recognition) Character recognition This course is ideal for individuals who are Intermediate or beginner R users who want to learn deep learning or Wannabe data scientists It is particularly useful for Intermediate or beginner R users who want to learn deep learning or Wannabe data scientists.
Enroll now: Deep Learning with Keras and Tensorflow in R
Summary
Title: Deep Learning with Keras and Tensorflow in R
Price: $19.99
Average Rating: 3.85
Number of Lectures: 37
Number of Published Lectures: 37
Number of Curriculum Items: 37
Number of Published Curriculum Objects: 37
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Basic knowledge about convolutional neural netowrks
- How to train a CNN to make predictions
- Image recognition (for example, human face recognition)
- Character recognition
Who Should Attend
- Intermediate or beginner R users who want to learn deep learning
- Wannabe data scientists
Target Audiences
- Intermediate or beginner R users who want to learn deep learning
- Wannabe data scientists
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)
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recognize wild animal images (we’ll use images with bears, foxes and mice)
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recognize special characters (distinguish an asterisk from a hashtag)
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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!
Course Curriculum
Chapter 1: Getting Started
Lecture 1: Introduction
Chapter 2: Basic Notions
Lecture 1: What Are Convolutional Neural Networks?
Lecture 2: Online Articles on the Topic
Lecture 3: Tools of the Trade
Lecture 4: Video Tutorials
Chapter 3: Building Classification Models with CNNS
Lecture 1: Classification Problem (Binomial Response): Data Preparation
Lecture 2: Classification Problem (Binomial Response): Building the Model
Lecture 3: Classification Problem (Binomial Response): Making Predictions
Lecture 4: Classification Problem (Multinomial Response): Data Preparation
Lecture 5: Classification Problem (Multinomial Response): Building the Model
Lecture 6: Classification Problem (Multinomial Response): Making Predictions
Chapter 4: Recognizing Human Faces From Trees
Lecture 1: Data Preparation
Lecture 2: Creating the Training Set and the Test Set
Lecture 3: Building the Model
Lecture 4: Making Predictions in the Test Set
Lecture 5: Making Predictions on New Data
Chapter 5: Recognizing Animals
Lecture 1: Recognizing Bears From Foxes: Data Preparation
Lecture 2: Recognizing Bears From Foxes: Training Set and Test Set
Lecture 3: Recognizing Bears From Foxes: Building the Model
Lecture 4: Recognizing Bears From Foxes: Making Predictions
Lecture 5: Recognizing Bears From Foxes: Making Predictions on New Data
Lecture 6: Recognizing Bears, Foxes and Mice: Data Preparation
Lecture 7: Recognizing Bears, Foxes and Mice: Training Set and Test Set
Lecture 8: Recognizing Bears, Foxes and Mice: Building the Model
Lecture 9: Recognizing Bears, Foxes and Mice: Making Predictions
Lecture 10: Recognizing Bears, Foxes and Mice: Making Predictions on New Data
Chapter 6: Telling Asterisks From Hashtags
Lecture 1: Data Preparation
Lecture 2: Training Set and Test Set
Lecture 3: Building the Model
Lecture 4: Making Predictions
Chapter 7: Recognizing Hand-Written Numbers
Lecture 1: Data Preparation
Lecture 2: Model Building
Lecture 3: Making Predictions
Lecture 4: Making Predictions on New Data
Chapter 8: Practice
Lecture 1: Data Sets Descriptions
Lecture 2: Practical Exercises
Chapter 9: Useful Links
Lecture 1: Download Your Resources Here
Instructors
-
Bogdan Anastasiei
University Teacher and Consultant
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 1 votes
- 3 stars: 1 votes
- 4 stars: 6 votes
- 5 stars: 8 votes
Frequently Asked Questions
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