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Intro

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Introduction to the Course​

Welcome to the world of Image Classification! 🌟

In this course, we're going to embark on an exciting adventure where you'll learn how to teach computers to recognize images. From setting up your coding environment to creating and training your own image classification model, you'll gain hands-on experience in the fascinating field of AI.

Get ready to explore, create, and conquer challenges as you build your very own image classification project. This isn't just any course—it's a fun-filled journey into the future of technology. So buckle up, because we're about to launch into an unforgettable learning experience!

Let's dive in and start our adventure!

Here's What You're in Store For!​

Are you ready for an adventure in the world of AI? Here’s what you’re in store for:

  • Setting Up Your Playground: Learn the basics of image classification and set up your coding environment on Google Colab.
  • Supercharging Your Setup: Load and preprocess the CIFAR-10 dataset.
  • Building and Training Your Model: Design, compile, and train a Convolutional Neural Network (CNN) model.
  • Evaluating and Improving: Evaluate your model and explore ways to enhance its performance.
  • Playing with Your Own Images: Test your model with new images and see the magic in action.

Get ready for a fun-filled journey into the world of AI and image classification!

Lesson Design​

Lesson 1: Introduction to Image Classification 🌟​

  • Basics of Image Classification: Discover the exciting world of image classification!
  • Google Colab Setup: Begin your quest by setting up your coding environment on Google Colab.
  • Loading the Dataset: Load and explore the CIFAR-10 dataset.

Lesson 2: Data Preprocessing ⚡​

  • Normalizing the Images: Prepare your images for training by normalizing them.
  • One-Hot Encoding Labels: Convert labels to one-hot encoded format.
  • Splitting the Data: Split the data into training and validation sets.

Lesson 3: Building the Convolutional Neural Network (CNN) 🛠️​

  • CNN Architecture: Learn about the architecture of Convolutional Neural Networks.
  • Building the Model: Create your CNN model.
  • Compiling the Model: Compile the model for training.

Lesson 4: Training the Model 🚀​

  • Training the CNN Model: Train your model with the training data.
  • Monitoring Progress: Track the accuracy and loss during training.

Lesson 5: Evaluating and Improving the Model 🧠​

  • Evaluating the Model: Assess your model's performance on test data.
  • Exploring Improvements: Implement data augmentation to improve the model.
  • Testing with New Images: Test your model with new images.