Cosine similarity measures the similarity between two vectors by calculating the cosine of the angle between them. It is widely used in text matching, search engines, recommendation systems, and AI models to compare documents, queries, and user preferences.
The vanishing gradient problem happens when training deep networks or RNNs, where the gradients (used to update weights) become very small as they move backward through the network. This makes it hard for earlier layers or time steps to learn anything, causing the model to forget long-term patterns.
Recurrent Neural Network is a type of neural network that is designed to work with data that comes in sequences, like a list of words, time-series data, or steps in a process.
Backpropagation Through Time (BPTT) is an extension of the standard backpropagation algorithm used to train recurrent neural networks (RNNs). It works by unrolling the RNN over a sequence of time steps and calculating gradients for each time step.
Backpropagation is a fundamental algorithm used to train artificial neural networks. It is used to minimize the error between the predicted output and the actual output by adjusting the network's weights.
In a neural network, weights are the adjustable parameters that control the strength of the connections between neurons in the layers.
The derivative of activation functions is crucial in the training of neural networks, especially during the backpropagation process.
The exponential function is unique in calculus because it is the only function that is its own derivative. This property makes it extremely important in various fields like physics, economics, and engineering.
Cross-entropy is a loss function commonly used in machine learning, particularly in classification tasks. It measures the difference between two probability distributions: the true labels and the predicted probabilities.
Neural Network is a machine learning model inspired by the human brain, designed to recognize patterns and solve complex tasks. It consists of interconnected units called neurons organized into layers
Activation functions in machine learning define how a neuron in a neural network processes input data and decides whether to pass it to the next layer.
