Convolutional Neural Network
Classify images using Convolutional Neural Network (CNN)
Development ,Data Science,Artificial Intelligence
Lectures -15
Duration -2.5 hours
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Course Description
Learn to build an image classification engine using a Convolutional Neural Network (CNN). CNN is a popular network where a machine can be trained to classify images based on patterns in the images. Once trained, it can be used to identify objects in the images.
A lot of smart researchers have already spent a lot of time building really good image classification networks like VGGNET, RESNET, and Inception V3. The networks are variants of CNN. These networks have been trained on an imagenet animal dataset. If your dataset requires a different type of image classification, you could just start with these networks and fine-tune them on your smaller dataset. This saves significant time and resources. Build a strong foundation in CNN with this tutorial for beginners.
- Understanding fundamentals Convolution.
- Understanding fundamentals of deep learning and CNN.
- Benefits of CNN.
- Learn how to apply CNN with a real example.
- Use Jupyter Notebook for step-by-step programming.
- Fine-tune accuracy of CNN.
- Build a real-life web application for dog vs cat classification.
- A Powerful Skill at Your Fingertips Learning the fundamentals of CNN puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.
No prior knowledge of CNN or deep learning is assumed. I will be covering topics like deep learning, Convolution, and CNN from scratch.
Jobs in the computer vision area are plentiful, and being able to learn transfer learning will give you a strong edge. CNN is state-of-the-art technology that can quickly help you achieve your goal.
Learning image classification with CNN will help you become a computer vision developer which is in high demand.
Content and Overview
This course teaches you how to build a dog vs cats classification engine using open-source Python and Jupyter framework. You will work along with me step by step to build the following answers:
- Introduction to Convolution.
- Introduction to CNN.
- Build a Jupyter notebook step by step using CNN.
- Build a real-world web application to find cat vs dog.
What am I going to get from this course?
- Learn CNN and build a dog vs cat image classification engine from a professional trainer from your own desk.
- Over 10 lectures teaching you how to build an image classification engine.
- Suitable for beginner programmers and ideal for users who learn faster when shown.
- Visual training method, offering users increased retention and accelerated learning.
- Breaks even the most complex applications down into simplistic steps.
- Offers challenges to students to enable reinforcement of concepts. Also, solutions are described to validate the challenges.
Goals
Classify images using Convolutional Neural Network (CNN)
Prerequisites
None.

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
3 Lectures
-
Introduction 08:08 08:08
-
Source Code Structure 05:06 05:06
-
About Author 05:42 05:42
Set up
2 Lectures

Basics of Convolution
4 Lectures

CNN and deep learning fundamentals
2 Lectures

Model Training
2 Lectures

Building Web Application
2 Lectures

Instructor Details

Evergreen Technologies
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