Overview
Discover how neural networks function in AI/ML models, exploring their structure, applications in image recognition, speech processing, and natural language understanding.
Syllabus
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- 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
Taught by
Tags
On-Demand
