NumPy ndarray: Speed & Properties | Artificial Intelligence using Python Topic
на канале: Skillscone
Welcome to Topic #050 of the Artificial Intelligence using Python course on Skills Cone! ⚡🔢
In this deep-dive into high-performance scientific computing, Dr. Yasir Khan introduces the NumPy library and its foundational data structure: the N-dimensional array (ndarray). Learn how Vectorized Operations execute parallel computations at C-speed, inspect essential array attributes including .shape, .ndim, and .dtype inside Jupyter Notebook, and watch a live speed benchmark comparing standard Python Lists against NumPy ndarrays across 1 Million elements.
⏱️ Video Timestamps (Chapters):
0:00 - Introduction to NumPy & N-Dimensional Arrays (ndarray)
1:25 - The Power of Vectorized Operations
2:28 - Core ndarray Properties: shape, ndim & dtype
3:29 - Installing & Importing NumPy as np
4:41 - Creating a 2D Array in Jupyter Notebook
5:37 - Inspecting Shape (2,3), Dimensions (2) & Data Type (int64)
6:42 - Performance Benchmark: Python List vs. NumPy ndarray (1M Items)
8:42 - Analyzing Benchmark Results: Why NumPy Wins
🎓 Subscribe to Skills Cone for full course walkthroughs, assignment solutions, and hands-on exercises covering Artificial Intelligence and Python!
▶️ Watch the Full Playlist: • Artificial Intelligence Using Python
#DigiSkills #ArtificialIntelligence #Python #NumPy #Ndarray #Vectorization #PythonBenchmark #ScientificComputing #DataScience #JupyterNotebook #SkillsCone #LearnPython #LearnAI #PythonProgramming Copyright Notice: All video content is owned by their respective copyright holders. This channel publishes videos solely for educational and informational purposes. For any copyright concerns, please contact us at [email protected].