- Input Layer: This layer receives the raw data.
- Hidden Layers: These layers perform computations and feature extraction, usually consisting of multiple layers to enhance learning capabilities.
- Output Layer: This layer produces the final output or prediction.
How Neural Networks Work
- Activation Functions: These functions determine whether a neuron should be activated or not based on its input.
- Learning Rate: This hyperparameter controls how much to change the model in response to the estimated error each time the model weights are updated.
Applications of Neural Networks
- Image and Video Recognition: Identifying and classifying images, driving advancements in facial recognition technology.
- Natural Language Processing: Enhancing the interaction between humans and machines through speech recognition and language translation.
- Healthcare: Assisting in diagnosing diseases and predicting patient outcomes using medical data.
- Finance: Fraud detection, risk management, and algorithmic trading.
Challenges and Future of Neural Networks
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