What You Need to Know Before
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Starts 8 June 2025 00:16
Ends 8 June 2025
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49 minutes
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Overview
Explore neural networks from a structural perspective, understanding their hypothesis class and forward propagation for making predictions.
Syllabus
- Introduction to Neural Networks
- Hypothesis Class in Neural Networks
- Neural Network Architecture
- Forward Propagation
- Making Predictions with Neural Networks
- Case Study: Forward Propagation in Action
- Summary and Q&A
Overview of neural networks and their applications
Importance of structural understanding in neural networks
Definition and importance of the hypothesis class
Factors affecting the hypothesis class
Examples of hypothesis classes in neural networks
Layers and neuron structure
Activation functions
Overview of common architectures (e.g., feedforward, convolutional, recurrent)
Explanation of forward propagation
Mathematical formulation of forward propagation in a neural network
Step-by-step example of forward propagation
Understanding the output layer and interpretation of predictions
Evaluation metrics for predictions (e.g., accuracy, loss functions)
Application of forward propagation to a sample dataset
Analyzing the impact of different architectures on predictions
Recap of key concepts
Open floor for questions and discussion on topics covered
Subjects
Computer Science
