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Building an Image Classification System Using CNN

Building an Image Classification System Using CNN

The Image Classification System project is a beginner-friendly deep learning project that demonstrates how to classify images using Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). Using the CIFAR-10 dataset, learners will preprocess image data, build deep learning models, compare their performance, and classify unseen images using TensorFlow and Keras.

6 Modules

40 Lessons

English

0.5 Hr

Reading Plan

MODULE 1

Introduction to Image Classification

6 mins

Introduction to Image Classification

1 min

What is Image Classification?

1 min

Importance of Image Classification

1 min

Understanding the CIFAR-10 Dataset

1 min

Aim of the Project

1 min

Expected Project Output

1 min

MODULE 2

Project Setup and Development Environment

5 mins

Project Setup and Development Environment

1 min

Tech Stack Used

1 min

Dataset Overview

1 min

Image Classification Project Input and Output

1 min

Why Use Convolutional Neural Networks (CNNs)?

1 min

MODULE 3

Deep Learning Fundamentals

9 mins

Deep Learning Fundamentals for Image Classification

1 min

Image Preprocessing

1 min

Artificial Neural Networks (ANN)

1 min

Convolutional Neural Networks (CNN)

1 min

Convolution and Pooling Layers

1 min

Activation Functions

1 min

Model Training and Evaluation

1 min

Image Prediction

1 min

Why CNNs Perform Better Than ANNs

1 min

MODULE 4

Building and Training the Artificial Neural Network (ANN)

9 mins

Building and Training the Artificial Neural Network (ANN)

1 min

Loading the CIFAR-10 Dataset

1 min

Exploring the Dataset

1 min

Displaying Sample Images

1 min

Data Preprocessing

1 min

Building the ANN Model

1 min

Compiling the ANN Model

1 min

Training and Evaluating an ANN Model for Image Classification

1 min

Predicting Image Classes Using ANN

1 min

MODULE 5

Building and Evaluating the Convolutional Neural Network (CNN)

6 mins

Building and Evaluating the Convolutional Neural Network (CNN)

1 min

Compiling the CNN Model

1 min

Training and Evaluating the CNN Model

1 min

Predicting Image Classes

1 min

Visualizing Predictions

1 min

Comparing ANN and CNN Performance

1 min

MODULE 6

Project Results and Assessment

5 mins

Project Results and Assessment

1 min

Live Output and Visualization Overview

1 min

Key Insights from the Analysis

1 min

Multiple Choice Questions (MCQs)

1 min

Resources

1 min

Contributors

Vishalini A

Building an Image Classification System Using CNN

Learn how to build an Image Classification System using TensorFlow and Keras. This beginner-friendly handbook covers image preprocessing, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), model evaluation, prediction, and visualization using the CIFAR-10 dataset.

Image Classification System Using CNN - Beginner Deep Learning Project

This handbook provides a hands-on introduction to image classification using deep learning. Learners will explore the CIFAR-10 dataset, preprocess image data, build and compare ANN and CNN models, evaluate their performance, and classify unseen images. By completing this project, learners will develop practical skills in TensorFlow, Keras, and computer vision.

Image Classification System Using CNN - Beginner Deep Learning Project

Prerequisites

This course is suitable for:

Basic knowledge of Python programming
Understanding of Data Science fundamentals
Familiarity with Machine Learning concepts
Basic understanding of Deep Learning and Neural Networks
Knowledge of image classification concepts (helpful but not mandatory)
A Google account to access Google Colab
Internet connection to access TensorFlow, Keras, and other required Python libraries

Run & Test your Code with our very own IDE that supports 16 languages

Open IDE

6 Modules

40 Lessons

English

0.5 Hr

Contributors

Vishalini A

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