Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable...
Implement YaRN (multiplies the attention matrix by a temperature factor) or ALiBi (attention penalty based on token distance) to extend the context length. Model merging: Merging trained models has become a popular way of creating performant models without any fine-tuning. The popular mergekit ...
Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable...
Implement YaRN (multiplies the attention matrix by a temperature factor) or ALiBi (attention penalty based on token distance) to extend the context length. Model merging: Merging trained models has become a popular way of creating performant models without any fine-tuning. The popular mergekit ...
Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable...
Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable...
This repository collects some codes that encapsulates commonly used algorithms in the field of machine learning. Most of them are based on Numpy, Pandas or Torch. You can deepen your understanding to related model and algorithm or revise it to get the cu
Linear Algebra: This is crucial for understanding many algorithms, especially those used in deep learning. Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of cont...
Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable...
Linear Algebra: This is crucial for understanding many algorithms, especially those used in deep learning. Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations. Calculus: Many machine learning algorithms involve the optimization of cont...