Table of Contents:
- Installation
- Version Check
- Array Initialization ~ 1-D, 2-D, 3-D
- Generating Data
- Vector Arrangements
- Data-Type — Conversions
- Math Operations
- Dot Product
- Matrix Multiplication
- Indexing and Slicing (2-D)
- Indexing and Slicing (2-D — Matrix)
- Reshaping and Transpose axes
- Concatenation
- Summing across axes
- Mean across axes
- Dimension Expansion & moving axes.
- Max (Min) and Argmax
- Slicing and Indexing (3-D Matrix)
1. Introduction
2. Version Check
3. Array Initialization ~ 1-D, 2-D, 3-D
Scalar and 1-D Array
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
2-D Vector Array
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
4. Generating Data
Scalar and 1-D Array
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
Draw random samples from the Normal distribution
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
Draw samples from the uniform distribution
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
5. Vector Arrangements
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
6. Data-Type — Conversions
uint8/16/32/64 ← → float8/16/32/64
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
7. Math Operations
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
8. Dot Product
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
9. Matrix Multiplication
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
10. Indexing and Slicing (2-D)
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
11. Indexing and Slicing (2-D — Matrix)
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
12. Reshaping and Transpose axes
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
13. Concatenation
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
14. Summing across axes
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
15. Mean across axes
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
16. Dimension Expansion & moving axes
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
17. Max (Min) and Argmax
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
18. Slicing and Indexing (3-D Matrix)
Numpy Implementation:
TensorFlow Implementation:
Torch Implementation:
Due to visualization constraints, I skipped the operations on the higher dimension parts.
I hope I was able to provide some visual understanding to some of the fundamental operations along with your choice of deep learning framework and I will add more detailed operations shortly.
Check out the Notebooks that contains the entire code implementation and feel free to break it.
Complete Code Implementation is available at,
Until then, see you next time.
Categories: Machine Learning, Deep Learning , Tags: #AI, #deepscopy
