A neural network of an AI is a system of interconnected artificial neurons organized in layers. Each layer consists of a number of neurons that receive inputs from the previous layer and send an output to the next layer. The output of each neuron depends on the inputs and is modified by an activation function.
The first layer of the neural network typically receives raw data as input, such as pixel values from images or audio signals. This raw data is then processed and transformed in the following layers to extract patterns and features that contribute to solving the specific problem for which the network was trained.
Training the neural network essentially involves optimizing the connections between neurons and the weights of the connections to improve the performance of the network. This is usually done using the backpropagation algorithm, which propagates the errors in the output signal back into the network and adjusts the weights accordingly.
Cloud networks are often used to train neural networks on larger datasets and with more resources. The work is distributed across many computers or virtual machines in the cloud. The individual nodes in the network regularly exchange information to ensure that each node has the latest versions of the weights and that the work is evenly distributed across all nodes. The use of cloud networks allows for more efficient training of large neural networks and can provide scaling advantages.