"Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains."
The fundamentals of artificial neural networks as a computing system and their various types.
Neuron: Basic building block of a neural network. It receives inputs, processes them, and produces output.
Activation Function: Function that decides the output of the neuron given its inputs.
Artificial Neural Network (ANN): A computational network which simulates the biological neural network of the human brain.
Supervised Learning: A type of learning in which the algorithm is trained with labeled data (input-output pairs).
Unsupervised Learning: A type of learning in which the algorithm is trained with unlabeled data and learns the underlying structure of the data.
Deep Learning: A subset of machine learning that uses neural networks with multiple hidden layers.
Backpropagation: A learning algorithm used to update the weights of the neural network to minimize the error between predicted and actual output.
Convolutional Neural Network (CNN): A type of neural network commonly used for image and video recognition tasks.
Recurrent Neural Network (RNN): A type of neural network that can model sequential data as it has the ability to remember and process previous inputs.
Gradient Descent: An optimization algorithm commonly used in conjunction with backpropagation for updating the weights of the neural network.
Dropout: A technique used to prevent overfitting in neural networks by randomly dropping out neurons during training.
Transfer Learning: A technique used to transfer the knowledge learned from a pre-trained neural network to a new and related problem.
Regularization: A technique used to prevent overfitting in neural networks by adding a penalty term to the error function.
Batch Normalization: A technique used to speed up the training process of neural networks by normalizing the input to each layer.
Hyperparameters: Parameters of the neural network that are set before training and affect the performance and behavior of the network.
Perceptron: :.
Autoencoder: :.
Hopfield Network: :.
Capsule Network: :.
"An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain."
"Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it."
"The 'signal' at a connection is a real number..."
"The output of each neuron is computed by some non-linear function of the sum of its inputs."
"The connections are called edges."
"Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection."
"Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold."
"Typically, neurons are aggregated into layers."
"Different layers may perform different transformations on their inputs."
"Signals travel from the first layer (the input layer)..."
"...to the last layer (the output layer)..."
"...possibly after traversing the layers multiple times."
"...built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains."
"...built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains."
"The 'signal' at a connection is a real number..."
"The weight increases or decreases the strength of the signal at a connection."
"Neurons and edges typically have a weight that adjusts as learning proceeds."
"Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold."
"Signals travel... from the first layer... to the last layer..."