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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