Neural Networks - Lecture 24b

Overview

Watch Lecture 24b:

Neural Networks on YouTube

Unravel the complexities of Neural Networks with this insightful lecture focused on understanding their structural intricacies. Lecture 24b delves into the hypothesis class and the mechanics of forward propagation essential for making informed predictions.

Perfect for those studying Artificial Intelligence or Computer Science.

Available on YouTube, this session is part of a comprehensive online course series brought to you by the university. Cement your knowledge by exploring these vital concepts involved in modern AI development.

Syllabus

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

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Subjects

Computer Science