Convolutional Neural Networks for Image Classification

🌟 Design Your Own Deep CNN for Image Recognition 🌟

Course Title: Convolutional Neural Networks for Image Classification

Course Headline:

Master the art of image recognition by designing, training, and testing your own Convolutional Neural Network (CNN) in real-time with live camera feed. 🤸‍♂️✨

Introduction: In this practical course, you'll design, train, and test your own Convolutional Neural Network (CNN) for the tasks of Image Classification. By the end of the course, you'll be able to build your own applications for real-time object detection and tracking. 📸🔬

Course Structure:

Step-by-Step Guide:

1. Implementing Convolution and Pooling Operations:

   • Learn to implement convolution and pooling operations on grayscale images using Numpy and 'for' loops.
   • Detect object edges and track movement in real-time by camera. 📷

2. Creating Custom Datasets:

   • Assemble images, compose your own dataset for classification tasks, and save it into a binary file.

3. Dataset Format Conversion:

   • Convert an existing dataset of Traffic Signs into the needed format for classification tasks.
   • Save the processed dataset into a binary file. 🚫🚩

4. Preprocessing Techniques:

   • Apply preprocessing techniques before training.
   • Produce and save datasets into separate binary files for easy access during the training phase. 🧪

5. Constructing CNN Models:

   • Design your own CNN models for classification tasks.
   • Select the appropriate number of layers and adjust other parameters for accurate classification.

6. Training CNNs:

   • Train constructed CNNs on new images.
   • Test trained models on completely new images in real-time via camera. 📹
   • Visualize the training process of filters from randomly initialized weights to their fully trained states.

7. Practice Test:

   • Take a practice test to consolidate your knowledge and skills learned throughout the course.

8. Bonus: Dataset Extension:

   • Generate up to 1 million additional images to extend the prepared dataset by applying techniques like image rotation, image projection, and brightness changing. 🚀

Course Goals:

The main goal of this course is to enhance your hard skills in Image Classification using Convolutional Neural Networks. With a focus on practical application and real-world problem-solving, you'll be well-equipped to tackle image recognition tasks with confidence. 💪

SMART Learning Objectives:

Each lecture is crafted with SMART objectives in mind, ensuring that you can:

• Specifically understand the objectives of each lecture.
• Measurably track your progress with clear metrics.
• Attainable goals that are within your reach with clear steps to follow.
• Result-oriented tasks that yield tangible outcomes by the end of the lecture.
• Time-oriented objectives, meaning you'll see results within a visible time frame. 🕒

Join us in this enlightening journey to master Convolutional Neural Networks for Image Classification. Let's turn your passion for AI into practical skills with this hands-on course. Enroll now and become an expert in image recognition! 🎓🎥🚀