Biological Neurons Vs Artificial Neurons?

• Biological neurons are the basic building blocks of the neurological system, whereas the artificial neurons are the neuron, also referred to as a perceptron, is a mathematical model used in artificial neural networks. 
  Although it functions in a computational environment, it mimics the way that biological neurons behave.
• Biological neurons uses chemical messages and electrical impulses whereas artificial neurons uses mathematical trained model.

Neural Networks in AI

1. Basic Structure:

Neural network can be defined as a layers of interconnected nodes, or neurons.
There are the three types of layers in this network:

• Input layer: Data is received by the input layer and then it is forwarded to the hidden layer
• Hidden Layers: Receives the data from the input layer and then after recognize patterns by processing data. 
• Output layer: Receives the processed result from hidden layer and generates the final product

2. Forward Propagation:

• Information travels from the input layer to the output layer via hidden layers. 
• Each neuron uses the activation function, weights, and biases to process information. 
• These calculations are what produce the outcome.

3. Activation Functions: 

• By introducing nonlinearity, these functions enable NNs to pick up intricate patterns. 
• Typical activation mechanisms: 
  1. Sigmoid: Converts inputs into a 0–1 range. 
  2. ReLU, or Rectified Linear Unit, outputs 0 otherwise and the input if it is affirmative. 
  3. The hyperbolic tangent, or tanh, maps inputs to a range of -1 to 1.

4. Backpropagation:

• The network modifies its weights and biases while it is being trained. 
• Errors in the expected and actual outputs spread in a backward direction. 
• For better performance, the network iteratively adjusts its settings.

5. Training:

• Neural Networks are trained using labeled data, or pairings of input and output. 
• Prediction error is measured by loss functions (e.g., mean squared error). 
• Weights are changed by optimization methods, such as gradient descent, in order to reduce loss.

6. Uniqueness and Deep Learning: 

• Deep Learning: Multi-layered neural networks are the deep architectures and directly implements deep learning. 
• Originality or Feature extraction: From unprocessed data, Neural Networks automatically extract pertinent features and learn from it automatically.
• Generalization: They adapt easily to new situations.
• End-to-End Learning:  Without the need for human feature engineering, Neural Networks learn straight from input to output.
   

8. Uses: 

• Image Recognition: Neural Networks are quite good at recognizing things in pictures. 
• RNNs are used in Natural Language Processing (NLP) to process sequences, such as text and speech. 
• They also play a vital role in the field of Finance, healthcare, recommendation systems, and other areas. 

Conclusion:

Neural networks can be recalled as the connected puzzle pieces that all contribute to the larger image of artificial intelligence.