Free Artificial Intelligence Courses
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COURSE RATING: 4.37 ⭐⭐⭐⭐
LEVEL: Beginner
Completion Certificate : YES
Duration : 5 Hours
Unlock the world of Digital Image Processing and computer vision with this free course. Learn about data augmentation, weight initialization, and regularization, as well as image processing using neural networks and image classification. Engage in exciting projects like smile and face detection, grayscale conversion, and image transformation. Enroll now and earn a free certificate upon completion.
Take your AI journey to new heights with Great Learning's toprated Artificial Intelligence Courses. Choose a program aligned with your career goals and receive a certificate of course completion. Don't miss this opportunity to enhance your skills and explore the limitless possibilities of Digital Image Processing. Enroll today and seize your AI future!
Course Outline
1. Introduction to Digital Image Processing:
- Overview of Digital Image Processing and its applications
- Understanding the importance of image processing in various fields
2. Data Augmentation:
- Techniques for augmenting image data
- Generating additional training samples through data augmentation
- Improving model performance and generalization using data augmentation
3. Weight Initialization:
- Importance of weight initialization in neural networks
- Common methods for weight initialization
- Optimizing network training with appropriate weight initialization techniques
4. Regularization:
- Understanding regularization in the context of image processing
- Techniques for preventing overfitting in neural networks
- Regularization methods such as L1 and L2 regularization
5. Image Processing using Neural Networks:
- Applying neural networks for image processing tasks
- Feature extraction and representation learning in image processing
- Utilizing convolutional neural networks (CNNs) for image processing
6. Image Classification Handson:
- Practical exercises on image classification using neural networks
- Implementing image classification algorithms in Python
- Evaluating model performance and accuracy for image classification tasks