An Artificial Neural Network (ANN) is a computational model inspired by the way biological neural networks work, particularly the human brain. ANNs are used in machine learning and artificial intelligence to solve complex problems, such as image recognition, natural language processing, and pattern detection.
Structure of Artificial Neural Networks
Neurons (Nodes):
- Similar to neurons in the brain, ANNs consist of interconnected units called neurons or nodes. Each node performs a simple calculation and passes the result to the next layer of nodes.
Layers:
- Input Layer: This layer receives input data (e.g., an image or a string of text).
- Hidden Layers: These intermediate layers process the input data. Deep learning networks have multiple hidden layers, enabling the system to model more complex data patterns.
- Output Layer: This layer produces the final prediction or decision (e.g., classification of an image or a predicted output).
Weights and Biases:
- Each connection between neurons has an associated weight, which represents the strength of the connection. Biases are additional parameters that adjust the output along with the weights.
- Activation Function:
- Neurons apply an activation function to the weighted sum of their inputs to determine the output. Common activation functions include the sigmoid, ReLU (Rectified Linear Unit), and tanh functions, which introduce non-linearity to allow the network to model complex relationships.
How ANNs Learn:
ANNs learn through a process called training, which involves adjusting the weights and biases to minimize error. The most common training method is backpropagation, where the network calculates the error in its prediction and uses this information to update the weights in the opposite direction of the error gradient.
Applications:
- Image and Speech Recognition: ANNs are used in applications like facial recognition, voice assistants, and handwriting analysis.
- Natural Language Processing (NLP): Used in machine translation, chatbots, and text generation.
- Medical Diagnosis: ANNs help in predicting diseases and analyzing medical images.
- Autonomous Systems: They’re crucial for self-driving cars and robotics.
