Training
Training in machine learning is the process of fitting a model's parameters to data so that the model can make accurate predictions or generate useful outputs.
Deep Learning
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Transfer Learning
Transfer learning is a family of machine learning methods in which information learned from one source domain or task is used while learning a target domain or task.
U-Net
U-Net is a convolutional neural network architecture designed for biomedical image segmentation.
Vanishing Gradient Problem
The vanishing gradient problem is a difficulty in training deep neural networks where the gradients used to update the network shrink exponentially as they are propagated backward through the layers, leaving…
Word Embedding
A word embedding is a learned representation of text in which words are mapped to dense vectors of real numbers in a continuous vector space, so that words with similar meanings are positioned close together.
XLNet
XLNet is a generalized autoregressive pretraining method for natural language processing that combines the strengths of autoregressive and autoencoding language models.
tf.keras
tf.keras is the high-level deep learning API built directly into the TensorFlow machine learning framework