Trends
An introduction to neural networks
They function through layers, including input, hidden, and output layers, facilitating learning and prediction.
Headline
They function through layers, including input, hidden, and output layers, facilitating learning and prediction.
Context
OUR TAKE Neural networks, although seemingly distant from our daily lives, intricately weave into our lives in imperceptible ways. They make it possible for us to immerse in content that is tailor-made for our interests, while empowering us smoothly engage with virtual assistants like Siri. Therefore, promoting understanding of them allows us to better use their capabilities to enrich our lives. –Audrey Huang, BTW reporter The article introduces the definition, operating principles and the types of neural networks.
Evidence
Pending intelligence enrichment.
Analysis
A neural network , or artificial neural network, is a type of computing architecture that is based on a model of how human brain functions. Neural networks consist of a collection of processing units called “ node s.” These nodes pass data to each other, just like how in a brain, neurons pass electrical impulses to each other. The networks are used in machine learning, a type of computer programs that acquire knowledge without definite instructions. Also read: Private wireless networks: Ownership, spectrum, and uses Also read: Ethernet dedicated lines vs. wireless networks Neural networks consist of numerous nodes distributed across at least three layers: an input layer, a hidden layer, and an output layer. Additionally, there can be multiple hidden layers apart from the input and output layers. Irrespective of their placement within the network, each node undertakes specific processing tasks or functions on the input received from the previous node or the input layer. Essentially, every node encompasses a unique mathematical formula, with individual variables weighted differently. If the outcome of applying this formula to the input surpasses a designated threshold, the node transfers data to the subsequent layer. Conversely, if the output falls below the threshold, no data is forwarded to the next layer.
Key Points
- Neural networks, inspired by the human brain, refers to a type of computing architecture that is based on a model of how human brain functions.
- They function through layers, including input, hidden, and output layers, facilitating learning and prediction.
- Types of neural networks include feed-forward, where data moves linearly; backpropagation, which refines predictions through continuous feedback; and convolutional, tailored for image analysis like AI image recognition.
Actions
Pending intelligence enrichment.
