Neural Networks Demo: Understanding AI/ML Models

Overview

Syllabus

• Introduction to Neural Networks
Overview of AI/ML Models
Historical Development of Neural Networks
Key Concepts: Neurons, Layers, and Activation Functions
• Neural Network Architecture
Structure and Components: Input, Hidden, and Output Layers
Types of Neural Networks: Feedforward, Recurrent, Convolutional
Activation Functions: Sigmoid, ReLU, Tanh
• Training Neural Networks
Data Preprocessing and Feature Scaling
Cost Functions and Error Minimization
Backpropagation and Gradient Descent
• Applications in Image Recognition
Understanding Convolutional Neural Networks (CNNs)
Image Processing Techniques
Exploring Real-world Use Cases
• Applications in Speech Processing
Overview of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
Speech Recognition Pipeline
Examples and Case Studies
• Applications in Natural Language Understanding
Introduction to Transformers and BERT
Sentiment Analysis and Language Translation
Practical Implementations and Tools
• Challenges and Best Practices
Overfitting and Underfitting Issues
Regularization Techniques: Dropout, L2 Regularization
Hyperparameter Tuning and Model Optimization
• Future Trends and Ethical Considerations
Advancements in Deep Learning Technologies
Ethical Implications in AI Deployment
Responsible AI and Fairness
• Conclusion and Further Learning
Recap of Key Concepts
Resources for Continued Exploration
Discussion on Future Directions in Neural Networks and AI

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Subjects

Computer Science