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Regularization techniques are used to reduce the complexity of the model in order to prevent overfitting. This can be achieved through methods such as L1 and L2 regularization, which add a penalty to the loss function based on the size of the weights.

Data Augmentation is a strategy used to increase the diversity of data available for training models, without actually collecting new data, by applying various transformations such as rotation, translation, scaling, and more.

Underfitting happens when a model is too simple, both in terms of the structure and the number of parameters, and cannot capture the underlying trend of the data.

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing certain actions and assessing the results it gets in the form of rewards or penalties.

Backpropagation is an algorithm used to calculate derivatives quickly in a neural network by going backwards from the output layer to the input layer, updating the weights to minimize the cost function.

In deep learning, attention mechanisms mimic cognitive attention by allowing the model to focus more heavily on certain parts of the input than others, improving the performance of models like RNNs on tasks like machine translation.

Gradient Descent is an optimization algorithm used for minimizing the cost function in a neural network by iteratively moving towards the minimum of the function gradient.

Batch Normalization is a technique used in neural networks to make the training faster and more stable through normalization of the input layer by re-centering and re-scaling.

Transfer Learning is a technique where a model developed for a certain task is reused as the starting point for a model on a second task; it is particularly useful in cases where there is a limited amount of data available for the second task.

Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.

A Deep Belief Network is a generative graphical model, or alternatively a type of neural network, composed of multiple layers of stochastic, latent variables with connections between the layers but not between units within each layer.

LSTMs are a special kind of RNN, capable of learning long-term dependencies. They were introduced to overcome the vanishing gradient problem that can occur in traditional RNNs.

Dropout is a regularization technique where randomly selected neurons are ignored during training, which helps in preventing overfitting by ensuring that the network does not become too reliant on any single neuron.

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

An activation function is a non-linear function applied to the output of a neuron, which determines whether it should be activated or not, adding non-linearity to the network and enabling it to learn complex relationships.

The learning rate is a hyperparameter that determines the step size at each iteration while moving toward a minimum of the cost function. It has a direct impact on the convergence of the gradient descent algorithm.

An autoencoder is a type of neural network used to learn efficient codings of unlabeled data; it includes an encoder that compresses the input into a latent-space representation, and a decoder that reconstructs the input data from the representation.

RNNs are a class of neural networks that are powerful for modeling sequence data such as time series or natural language, with the capability of using information from previous inputs in the network's internal state to process sequences of inputs.

CNNs are a class of deep neural networks, most commonly applied to analyzing visual imagery, with convolutional layers that automatically and adaptively learn spatial hierarchies of features from input images.