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Intermediate

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From Python to Numpy
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Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra). Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach. Practice and test yourself along the way with in-browser coding challenges, quizzes, and more. This course is intended for users who are already familiar with intermediate level Python.

If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your...Show More

Content

45 Lessons17 Quizzes1 Challenge

Introduction

7 Lessons

Get familiar with NumPy fundamentals, including array creation, reshaping, indexing, and vectorization.

Broadcasting in NumPy

NumPy Vectorization

Readability vs. Speed

Anatomy of an Array

5 Lessons

Look at the foundational elements of NumPy arrays, memory layout, views, copies, and optimization.

Introduction

Memory layout

Views and Copies

Coding Example: How to find if one vector is view of the other?

Solution Review

Code Vectorization

13 Lessons

Examine methods to enhance Python code efficiency using NumPy for various simulations.

Introduction

Uniform Vectorization

Coding Example: Game of life (Python approach)

Coding Example: Game of life (NumPy approach)

Coding Example: Reaction-Diffusion

Temporal Vectorization

Coding Example: The Mandelbrot Set (Python approach)

Coding Example: The Mandelbrot Set (NumPy approach)

Coding Example: Minkowski-Bouligand Dimension

Spatial Vectorization

Coding Example: Implement the behavior of Boids (Python approach)

Coding Example: Implement the behavior of Boids (NumPy approach)

Conclusion

Problem Vectorization

9 Lessons

Find out about problem vectorization to enhance algorithmic efficiency in Python and NumPy.

Introduction

Coding Example: Find shortest path in a maze

Coding Example: Find shortest path in a maze (Breadth-First approach)

Coding Example: Find shortest path in a maze (Bellman-Ford approach)

Coding Example: Fluid Dynamics

Coding Example: Blue Noise Sampling

Coding Example: Blue Noise Sampling using DART method

Coding Example: Blue Noise Sampling using Bridson method

Conclusion

Custom Vectorization

5 Lessons

Dig into custom vectorization techniques using Typed Lists and memory-aware NumPy arrays.

Typed list

Coding Example: Modifying the list

Memory-aware Array: Glumpy

Memory-aware Array: Array Subclass in GPUData class

Conclusion

Beyond NumPy

4 Lessons

Implement optimized Python techniques and explore advanced libraries beyond NumPy for enhanced performance.

2 Lessons

Master the steps to effectively leverage NumPy for creative and efficient problem-solving.

About the Author

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Course Author:

Nicolas P. Rougier

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