NumPy Reshape & View Methods: 1D to 2D & 3D Arrays (تغيير شكل المصفوفات) #10
Welcome back! In this lesson, we master the Reshape function and the View method to manipulate array dimensions. Understanding how to transition data from 1D to 2D and 3D is a fundamental skill for preparing data for Machine Learning models.
In this tutorial, you will learn:
✅ Shape Shifting with View: Using .view() and .T (Transpose) to flip rows and columns without affecting the original data.
✅ The Reshape Rule: The mathematical logic behind dimensions (Rows × Columns must equal the total length).
✅ 1D to 2D Transformation: How to turn a flat list into a matrix (e.g., 2x5 or 1x10).
✅ 1D to 3D Transformation: Planning your data's depth, rows, and columns for complex structures. ✅ Data Integrity: Using the .base property to verify your array's memory ownership.
00:00 Channel Intro
00:14 Introduction: Reviewing the View Method
00:41 Using .view() to Change Array Shape
01:21 Understanding Transpose (.T) for Matrix Manipulation
02:04 Checking Data Ownership with .base
02:41 Introduction to the reshape() Function
03:15 Rules for Reshaping 1D Arrays into 2D
04:31 Practical Example: 1x10 and 2x5 Reshapes
05:10 The Math Behind 1D to 3D Reshaping
06:38 Conclusion & Next Video Preview
🚀 Resources:
GitHub (Source Code): https://github.com/rubada/Machine-Lea...
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