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Underfitting is the inverse of overfitting, meaning that the statistical model or machine learning algorithm is too simplistic to accurately capture the patterns in the data. A sign of underfitting is that there is a high bias and low variance detected in the current model or algorithm used (the inverse of overfitting: low bias and high variance).
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
The cost of each time step can be computed separately. The figure above shows how the cost at time t + 3 {\displaystyle t+3} can be computed, by unfolding the recurrent layer f {\displaystyle f} for three time steps and adding the feedforward layer g {\displaystyle g} .
Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [ 2 ] Its popularity results in a vast ecosystem of libraries , including for deep learning , such as PyTorch , TensorFlow , Keras , Google JAX .
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In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
The form the population iteration, which converges to , but cannot be used in computation, while the form the sample iteration which usually converges to an overfitting solution. We want to control the difference between the expected risk of the sample iteration and the minimum expected risk, that is, the expected risk of the regression function: