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We design this course with 9 lessons to help beginners can quickly understand and utilize this useful library.

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Learn NumPy

We design this course with 9 lessons to help beginners can quickly understand and utilize this useful library.

Lessons

Lesson 1: Basics & Array Creations

Lesson 2: How to print array? & Format settings

Lesson 3: Basic calculation in NumPy(addition, subtraction, multiplication and division)

Lesson 4: Indexing & Slicing & Iterating & From Function

Lesson 5: Shape Manipulation & Stack & Split matrices

Lesson 6: Simple assignments & Views & Deep copy

Lesson 7: Indexing with an array of indices

Lesson 8: Indexing with a boolean array

Lesson 9: ix() & Linear algebra & Repeat & Stack

Notes

Note 1: The padding on matrix

  • This article will show how to use the "pad()" function of Numpy, and utilize it in building convolutional neural networks.
  • File Name: numpy_note-01-Padding-function-in-numpy.ipynb
  • Link: Numpy 筆記-#01 卷積神經網路的Padding

Note 2: Save the variables as .npz file

  • We provided how to save the variables as a npz file, and demonstrated how to load npz file. In addition to this, we also compared the time costs of numpy.savez and numpy.savez_compressed.
  • File Name: numpy_note-02-save-variables-as-npz.ipynb
  • Link: Numpy 筆記-#02 另存變數為 .npz

How to use?

  • Just fork to your own github or download this repository directly. And run the jupyter file that you interest lesson.

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We design this course with 9 lessons to help beginners can quickly understand and utilize this useful library.

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