Certificate in Convolutional Neural Networks for Vision
-- ViewingNowCertificate in Convolutional Neural Networks for Vision: This certificate course highlights the significance and application of Convolutional Neural Networks (CNNs) in the field of computer vision. With the increasing demand for automation and image processing in various industries, the course is essential for professionals seeking to expand their skills in deep learning.
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⢠Introduction to Convolutional Neural Networks (CNNs): Understanding the basics of CNNs, their architecture, and components such as convolutional layers, pooling layers, and fully connected layers.
⢠Image Processing and Feature Extraction: Learning about image processing techniques, filters, and transformations, as well as how CNNs extract features from images.
⢠Convolutional Layer Design: Exploring the design principles of convolutional layers, including kernel sizes, strides, and padding, and their impact on the receptive field of CNNs.
⢠Pooling Layers and Invariance: Understanding the role of pooling layers in reducing the spatial dimensions of feature maps, increasing invariance to translations and deformations.
⢠Activation Functions and Normalization: Learning about activation functions such as ReLU, sigmoid, and tanh, as well as normalization techniques such as batch normalization.
⢠Training CNNs with Backpropagation: Understanding the training process of CNNs, including the use of backpropagation for updating weights, and techniques such as gradient descent, stochastic gradient descent, and Adam.
⢠Regularization Techniques for CNNs: Exploring regularization techniques such as dropout, weight decay, and data augmentation, and their impact on the generalization performance of CNNs.
⢠Transfer Learning and Fine-Tuning: Learning about transfer learning, where pre-trained CNNs are used as a starting point for new tasks, and fine-tuning, where pre-trained CNNs are further trained for specific tasks.
⢠Applications of CNNs in Computer Vision: Exploring various applications of CNNs in computer vision, such as image classification, object detection, segmentation, and generative models.
⢠Ethical Considerations in Computer Vision and AI: Understanding the ethical considerations of using CNNs and computer vision, including issues related to
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