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Theoretical models are simplified representations of complex systems or phenomena that help in understanding, explaining, and predicting behaviors within a given context. They serve as frameworks for developing hypotheses and guiding empirical research, often requiring refinement through testing and validation against real-world data.
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A neural network is a computational model inspired by the human brain's network of neurons, designed to recognize patterns and solve complex problems through learning from data. It consists of layers of interconnected nodes that adjust their weights based on input data to improve accuracy over time.
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An activation function in a neural network introduces non-linearity into the model, enabling it to learn complex patterns in the data. It determines the output of a node given an input or set of inputs, which is crucial for the network's ability to approximate any continuous function.
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Softmax is a mathematical function that converts a vector of real numbers into a probability distribution, where each value is in the range (0, 1) and the sum of all values is 1. It is commonly used in machine learning, especially in the final layer of a neural network classifier, to predict the probabilities of different classes.
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Regression is a statistical method used to model and analyze the relationships between a dependent variable and one or more independent variables. It is widely used for prediction and forecasting, allowing for the understanding of how changes in predictors influence the outcome variable.
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Classification is a supervised learning approach in machine learning where the goal is to predict the categorical label of a given input based on training data. It is widely used in applications such as spam detection, image recognition, and medical diagnosis, where the output is discrete and predefined.
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A decision boundary is a hypersurface that partitions the underlying vector space into two sets, one for each class in a binary classification problem. It is determined by the model and represents the threshold at which the model switches from predicting one class to another.
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A loss function quantifies the difference between the predicted output of a machine learning model and the actual output, guiding the model's learning process by penalizing errors. It is essential for optimizing model parameters during training, directly impacting the model's performance and accuracy.
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Backpropagation is a fundamental algorithm in training neural networks, allowing the network to learn by minimizing the error between predicted and actual outputs through the iterative adjustment of weights. It efficiently computes the gradient of the loss function with respect to each weight by applying the chain rule of calculus, enabling the use of gradient descent optimization techniques.
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Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent, or negative gradient, of the function. It is widely used in machine learning to update model parameters and minimize the loss function, ensuring the model learns efficiently from data.
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Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers as if they were true patterns, which results in poor generalization to new, unseen data. It is a critical issue because it can lead to models that perform well on training data but fail to predict accurately when applied to real-world scenarios.
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Forward propagation is the process in a neural network where input data is passed through the network layer by layer to produce an output. It is crucial for computing the predicted output and is the first step before calculating loss and performing backpropagation to update the model's weights.
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Feedforward Neural Networks are a type of artificial neural network where connections between the nodes do not form a cycle, meaning data moves in one direction from input to output. They are the simplest form of neural networks and are primarily used for pattern recognition and classification tasks.
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