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neural networks - Coggle Diagram
neural networks
Artificial Neural Networks (ANNs)
Definition and Functionality
Mimic human brain processes for cognitive tasks.
Process inputs,
outputs based on connection weights.
Historical Development
Hebbian learning.
Backpropagation.
Structure of ANNs
Input layers
hidden layers
output layers
Complexity increases with hidden layers.
Various architectures:
convolutional,
recurrent,
etc.
Learning Process
Forward
Backpropagation.
Adjusts weights
iteratively
minimize errors.
Challenges and Limitations
Limited to specific tasks.
Challenges in facial and speech recognition.
Data Handling
Training
validation
testing sets.
More data enhances accuracy.
Introduction
Human Intelligence & Machine Inspiration
Brain enables perception,
learning,
memory,
planning,
action.
Early flight attempts inspired by birds.
Module Focus: Neural Networks
Explore inner workings
image recognition
Generative AI
Deep learning
accurate problem-solving.
Machine Learning
supervised,
unsupervised,
reinforcement learning
Highlights deep learning
mechanics,
error processing
weight allocation.
ANN Applications
Image Recognition
Input encoded into ANN, processed through layers.
Challenges:
lighting,
poses
facial features.
Speech Recognition and NLP
language understanding
generation
translation.
Challenges: varied accents, pronunciation.
Other Applications
ANNs in various domains:
law enforcement,
autonomous vehicles,
etc.
Conclusion
Impact of ANNs
Drive innovations in multiple fields.
Complexity depends on the problem.
Data's Role
More data improves performance.
Future Topics
Identifying handwritten digits.
Content generation using generative deep learning.