The Ultimate Artificial Neural Network Quiz

The basic building block of an artificial neural network (ANN) is a neuron. Neurons are interconnected units that process and transmit information within the network.

Supervised Learning in ANNs involves training with labeled data to minimize prediction errors. The network is provided with input-output pairs during training, and it learns to make accurate predictions based on the given labels.

Overfitting in an ANN occurs when the model performs well on training data but poorly on unseen data. This happens when the model becomes too complex and starts to memorize the training data rather than learning general patterns.

The ANN architecture primarily used for image and video recognition tasks is the Convolutional Neural Network (CNN). CNNs use convolutional layers to automatically learn and extract relevant features from images.

The term "deep" in Deep Learning refers to ANNs with a large number of layers. Deep Learning involves networks with many hidden layers, allowing them to learn complex patterns and representations.

The activation function commonly used for binary classification problems is the Sigmoid function. The Sigmoid function maps the output of a neuron to a range between 0 and 1, making it suitable for binary decision-making.

The purpose of the "activation function" in an ANN is to introduce non-linearity to the network. Non-linear activation functions allow ANNs to approximate complex functions and learn non-linear relationships in the data.

In an ANN, the "weights" determine the strength of connections between neurons. These weights play a crucial role in the learning process and affect the influence of each neuron in the network.

The ANN architecture well-suited for sequential data, such as time series or natural language, is the Recurrent Neural Network (RNN). RNNs have loops that allow information to persist across time steps, making them effective for sequential data analysis.

The ANN architecture that connects each neuron from one layer to every neuron in the subsequent layer is called the Multilayer Perceptron (MLP). MLP is a feedforward neural network and is the most basic form of a deep neural network.

The process of an ANN making predictions based on learned patterns is called forward propagation. In forward propagation, input data is passed through the network, and activations are computed for each neuron until the final output is obtained.

In an ANN, the "bias" is used to shift the output of a neuron. Bias allows the activation function to be flexible in terms of how it responds to different inputs.

The process of finding the best set of weights and biases in an ANN is called backpropagation. Backpropagation involves computing the gradients of the loss function with respect to the model's parameters and adjusting them accordingly.

The part of an ANN that is responsible for adjusting the model's parameters during training is the optimizer. The optimizer is an algorithm that updates the weights and biases of the network to minimize the error or loss function.