Artificial Intelligence: Neural Networks
How can we bridge the gap between artificial neural networks and the intricate complexities of human cognition, and what insights can we gain about the nature of human intelligence through the study and development of these machine learning models?
Neural networks were inspired by the functionalities of the human brain. In a way they were modeled after it. Just as the brain has these synapses and routes between one nueron to the next, machine learning scientists have been trying to crackdown on the great phenomenon in the observable universe: the human brain.
But what makes it even more compelling is the fact we simply do not fully understand the brain in its entirety. Consciousness? The meaning of life? A series of simple questions to complex answers.
Neural Networks: A Simplified Explanation
Neural networks, inspired by the human brain, are powerful tools in artificial intelligence and deep learning. They consist of interconnected layers of nodes, or artificial neurons. These layers include:
- Input Layer: Receives input data.
- Hidden Layers: Process the input data through complex calculations.
- Output Layer: Produces the final output or prediction.
How Neural Networks Work:
- Node as a Linear Regression Model: Each node functions as a simple linear regression model. It takes weighted inputs, adds a bias, and applies an activation function to produce an output.
- Feedforward Networks: Information flows in one direction, from the input layer to the output layer.
- Training Neural Networks:
- Supervised Learning: The network learns from labeled data, adjusting its weights and biases to minimize the difference between its predictions and the correct outputs.
- Cost Function: Measures the error between the predicted and actual values.
- Gradient Descent: An optimization algorithm that iteratively adjusts the weights and biases to reduce the cost function.
Types of Neural Networks:
- Feedforward Neural Networks: The simplest type, used for tasks like classification and regression.
- Convolutional Neural Networks (CNNs): Specialized for image and video recognition, they use convolution and pooling operations to extract features from the input data.
- Recurrent Neural Networks (RNNs): Designed to process sequential data, like time series or natural language, by using feedback loops to maintain information about past inputs.
In essence, neural networks are versatile tools that can learn complex patterns and make accurate predictions, making them essential for various AI applications, from image and speech recognition to natural language processing and self-driving cars.
[1]: Brian Yu, David J. Malan; Harvard University CS50's Introduction to Artificial Intelligence with Python
[2]: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor; An Introduction to Statistical Learning with Applications in Python
[3]: Nick McCullum; Deep Learning Neural Networks Explained in Plain English with freeCodeCamp.org
