Global Certificate in Convolutional Neural Network Training
-- ViewingNowThe Global Certificate in Convolutional Neural Network (CNN) Training course is a comprehensive program designed to provide learners with essential skills in CNNs, a popular deep learning algorithm. This course emphasizes the importance of CNNs in various applications such as image and video recognition, self-driving cars, and medical imaging analysis.
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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 Computer Vision: Learning about image processing techniques and how computers "see" and interpret visual data. This unit covers topics such as edge detection, image filtering, and feature extraction.
⢠Convolutional Layer Design: Exploring different convolutional layer designs, such as strided convolutions, dilated convolutions, and transposed convolutions. This unit also covers the importance of kernel size, padding, and stride length in convolutional layers.
⢠Pooling Layers and Spatial Pyramid Pools: Understanding the role of pooling layers in reducing the spatial dimensions of feature maps and their impact on computational complexity. This unit also covers the concept of spatial pyramid pooling and its benefits.
⢠Fully Connected Layers and Classification: Learning about fully connected layers in CNNs and how they are used for classification tasks. This unit covers the use of activation functions, such as softmax and ReLU, and how they affect classification accuracy.
⢠Regularization Techniques for CNNs: Understanding regularization techniques, such as dropout and weight decay, and their impact on overfitting in CNNs. This unit also covers the importance of data augmentation in improving model generalization.
⢠Optimization Techniques for CNN Training: Learning about optimization techniques, such as stochastic gradient descent (SGD) and adaptive moment estimation (Adam), and their impact on CNN training. This unit also covers the importance of hyperparameter tuning and learning rate scheduling.
⢠Transfer Learning and Fine-Tuning: Exploring the concept of transfer learning and how pre-trained CNNs can be fine-tuned
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