>neural nets, or cognitive models to explain how to
>chat within the limits of A. L. I. C. E.'s 40,000 categories
>A. L. I. C. E.'s 40,000 categories
KEK@40.000 ..that's all folks kekek
An artificial neural network is a biologically inspired computational model that is patterned after the network of neurons present in the human brain. Artificial neural networks can also be thought of as learning algorithms that model the input-output relationship. Applications of artificial neural networks include pattern recognition and forecasting in fields such as medicine, business, pure sciences, data mining, telecommunications, and operations managements.
An artificial neural network transforms input data by applying a nonlinear function to a weighted sum of the inputs. The transformation is known as a neural layer and the function is referred to as a neural unit. The intermediate outputs of one layer, called features, are used as the input into the next layer. The neural network through repeated transformations learns multiple layers of nonlinear features (like edges and shapes), which it then combines in a final layer to create a prediction (of more complex objects). The neural net learns by varying the weights or parameters of a network so as to minimize the difference between the predictions of the neural network and the desired values. This phase where the artificial neural network learns from the data is called training.
Neural networks where information is only fed forward from one layer to the next are called feedforward neural networks. On the other hand, the class of networks that has memory or feedback loops is called Recurrent Neural Networks.
All classification tasks depend upon labeled datasets; that is, humans must transfer their knowledge to the dataset in order for a neural network to learn the correlation between labels and data. This is known as supervised learning.
Detect faces, identify people in images, recognize facial expressions (angry, joyful)
Identify objects in images (stop signs, pedestrians, lane markers…)
Recognize gestures in video
Detect voices, identify speakers, transcribe speech to text, recognize sentiment in voices
Classify text as spam (in emails), or fraudulent (in insurance claims); recognize sentiment in text (customer feedback)
Any labels that humans can generate, any outcomes that you care about and which correlate to data, can be used to train a neural network.