Tensor Operations

Tensor operations are essential in Machine Learning and Deep Learning.

Numpy & Tensorflow

They are integrated in numpy, tensorflow and other frameworks. In this repo we review the main tensor ops with these two frameworks.

Featuring R2, R3 and >R3 tensors use cases.

Table of Contents

• Numpy
  • Tensor linear algebra
    • Tensor products
      • Dot
      • Inner
      • Outer
      • Matmul
      • Tensordot
  • Tensor manipulation
    • Changing tensor shape
      • Reshape
      • Ravel
    • Transpose-like operations
      • Moveaxis
      • Rollaxis
      • Swapaxes
      • Transpose
    • Changing number of dimensions
      • Expand dims
      • Squeeze
    • Joining tensors
      • Concatenate
      • Stack
    • Splitting tensors
      • Split
• Tensorflow
  • Tensor linear algebra
    • Tensor products
      • Matmul
      • Tensordot
  • Tensor manipulation
    • Changing tensor shape
      • Reshape
      • Flatten (reshape special case)
    • Transpose-like operations
      • Rollaxis
      • Transpose
    • Changing number of dimensions
      • Expand dims
      • Squeeze
    • Joining tensors
      • Concatenate
      • Stack
    • Splitting tensors
      • Split