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MIT Deep Learning

Linear Regression

RNN

Backpropagation

LSTM

  1. Maintain a cell state
  2. Use gates to control

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No, the classifier for a machine learning model, such as XGBoost, is typically part of supervised learning, not unsupervised learning. Here’s a brief overview to clarify:

Supervised Learning

  • Definition: Involves training a model on a labeled dataset, where the outcome or target variable is known.
  • Examples: Classification (e.g., XGBoost classifier), Regression.
  • Algorithms: XGBoost, Random Forest, SVM, Neural Networks, etc.

Unsupervised Learning

  • Definition: Involves training a model on data without labeled outcomes, aiming to find hidden patterns or intrinsic structures.
  • Examples: Clustering, Dimensionality Reduction, Anomaly Detection.
  • Algorithms: K-Means, PCA, Autoencoders, etc.

XGBoost

  • Category: Supervised Learning.
  • Use Cases: Can be used for classification (e.g., predicting categories) and regression (e.g., predicting continuous values).

In your case, since you're dealing with a classifier and predicting temperature adjustments, you are working with a supervised learning approach.

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UW HYAK Notes

Request for GPU

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This article offers a detailed explanation of transformers, a revolutionary architecture in natural language processing (NLP) that has significantly advanced the capabilities of large language models (LLMs). It covers the structure, mechanics, and key components of transformers, including the encoder, decoder, attention mechanisms, and embeddings.

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