Tensor Operations in Advanced Machine Learning
Tensor operations are a fundamental part of advanced machine learning. They allow for complex computations and manipulations of multi-dimensional data structures known as tensors. In this tutorial, we will explore the various operations that can be performed on tensors and their applications in machine learning.
Basic Tensor Operations
Addition and Subtraction
Tensor addition and subtraction are straightforward. They involve adding or subtracting corresponding elements of two tensors of the same shape.
Multiplication and Division
Tensor multiplication and division are also basic operations. They are similar to scalar multiplication and division, but they operate on multi-dimensional arrays.
Advanced Tensor Operations
Transpose
The transpose of a tensor is a new tensor that is obtained by flipping the rows and columns of the original tensor.
Matmul
Matrix multiplication (or matmul) is an operation that takes two tensors and returns their matrix product.
Contraction
Tensor contraction is a powerful operation that reduces the number of dimensions of a tensor by summing over one or more dimensions.
Applications
Tensor operations are used in various machine learning applications, including:
- Deep Learning: Tensors are used to represent and manipulate data in neural networks.
- Computer Vision: Tensors are used to represent images and perform operations like convolution and pooling.
- Natural Language Processing: Tensors are used to represent text and perform operations like word embeddings and sentence encoding.
For more information on tensor operations and their applications in machine learning, check out our Introduction to Deep Learning.