Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Training Provider: SINGAPORE MANAGEMENT UNIVERSITY
Course Reference: TGS-2024051185
S$660
Original: S$2,200
Save S$1,540
About This Course
This module focuses on advanced neural network architectures essential for tasks like image classification and sequence processing. Divided into four sessions, participants will explore the concepts of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), implementation techniques, and real-world applications.
What You'll Learn
- Describe the fundamental concepts of Convolutional Neural Networks (CNNs), including convolutional layers, filters, and pooling layers, and their role in tasks like image classification and object detection.
- Implement CNNs using PyTorch for tasks like image classification, leveraging common architectures like seminal convolutional neural network architecture (LeNet), Very Deep Convolutional Networks (VGG), and Residual Networks (ResNet).
- Describe the fundamentals of Recurrent Neural Networks (RNNs) and their applications in processing sequential data.
- Implement RNNs using PyTorch for tasks like sequence classification, employing architectures like Long short-term memory (LSTM).
- Implement CNNs using PyTorch for tasks like image classification, leveraging common architectures like seminal convolutional neural network architecture (LeNet), Very Deep Convolutional Networks (VGG), and Residual Networks (ResNet).
- Describe the fundamentals of Recurrent Neural Networks (RNNs) and their applications in processing sequential data.
- Implement RNNs using PyTorch for tasks like sequence classification, employing architectures like Long short-term memory (LSTM).
Entry Requirements
Nil
Course Details
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Note: To apply for this course, visit the SkillsFuture website or contact the training provider directly.
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