Introduction to CNN Training

Overview

This beginner-friendly course on Convolutional Neural Networks (CNNs) equips you with essential skills to understand deep learning fundamentals and apply them to real-world image recognition tasks. Learn how CNNs power modern AI applications and gain practical experience through guided lab demos. Build confidence in designing, training, and implementing CNN models effectively. By the end of this course, you will be able to: Understand CNN Basics: Explain what CNNs are and their role in deep learning and computer vision Explore Core Components: Learn about convolution, ReLU, and pooling layers in CNNs Recognize Image Processing: Understand how CNNs detect and classify image features Apply CNN Models: Build and implement CNN models through hands-on guided labs Gain Practical Skills: Develop expertise to handle real-world image classification projects Ideal for beginners, and professionals interested in AI, computer vision, and deep learning.

Syllabus

Fundamentals of CNN

Understand the fundamentals of Convolutional Neural Networks (CNNs) to build expertise in deep learning and computer vision. Learn how CNNs recognize images and explore core components like convolution, ReLU, and pooling layers. Gain practical skills through guided lab demos and implement CNN models for real-world image classification tasks.

Hands-On with CNN

Gain practical experience in Convolutional Neural Networks through step-by-step lab demos. From basics to advanced implementation, this module walks you through multiple hands-on exercises across five demos, helping you build, test, and apply CNN models effectively in real-world scenarios.