Free Artificial Intelligence Courses-Convolutional Neural Networks


Free Artificial Intelligence Courses

Free Artificial Intelligence Courses-Convolutional Neural Networks

COURSE 3 : Convolutional Neural Networks
COURSE RATING: 4.75 ⭐⭐⭐⭐⭐
LEVEL: Intermediate
Completion Certificate : YES
Duration : 3 Hours

Convolutional Neural Networks (CNNs) have emerged as a powerful tool in the field of machine learning and deep neural networks. This course focuses on understanding and applying CNNs to achieve stateoftheart results in various domains. CNNs excel in image recognition tasks and outperform other deep learning algorithms in this area. By breaking down images into perceptive features, CNNs efficiently learn and classify visual images.

Through out the course, we will explore topics such as digital images, the convolution process, and pooling techniques like max and average pooling. We will delve into kernels, filters, and feature maps involved in the convolution process of CNNs. Additionally, we will cover the concept of batch normalization, a crucial aspect of deep learning.

Join this course to gain insights into CNNs, their workings, and their applications. Enhance your understanding of artificial intelligence and explore the exciting possibilities it holds for the future.

Discover our toprated courses in Artificial Intelligence today.

Course Outline

 Digital Images Overview:

  •    Understanding the basics of digital images
  •    Image representation and formats
  •    Pixel values and color channels

 Image as a Function:

  •    Treating images as mathematical functions
  •    Image transformation and manipulation
  •    Image scaling and resizing

 Edge as a Feature:

  •    Detecting edges in images
  •    Edge detection algorithms and techniques
  •    Applying edge detection in image processing

 Digital Noise:

  •    Types of noise in digital images
  •    Common sources of noise
  •    Noise reduction and image denoising techniques

 Convolution Process:

  •    Introduction to convolution operation
  •    Convolutional filters and kernels
  •    Understanding feature extraction through convolution


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