High-Performance Computing with Python 3.x

High-Performance Computing with Python 3.x, available at $44.99, has an average rating of 4.6, with 44 lectures, based on 158 reviews, and has 1033 subscribers.

You will learn about Use lambda expressions, generators, and iterators to speed up your code. A solid understanding of multiprocessing and multithreading in Python. Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations. Load large data using Dask in a distributed setting. Leverage the power of Numba to make your Python programs run faster. Build reactive applications using Python. This course is ideal for individuals who are This course will help Python Programmers, Data Analysts and aspiring Data Science professionals. It is particularly useful for This course will help Python Programmers, Data Analysts and aspiring Data Science professionals.

Enroll now: High-Performance Computing with Python 3.x

Summary

Title: High-Performance Computing with Python 3.x

Price: $44.99

Average Rating: 4.6

Number of Lectures: 44

Number of Published Lectures: 44

Number of Curriculum Items: 44

Number of Published Curriculum Objects: 44

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Use lambda expressions, generators, and iterators to speed up your code.
A solid understanding of multiprocessing and multithreading in Python.
Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations.
Load large data using Dask in a distributed setting.
Leverage the power of Numba to make your Python programs run faster.
Build reactive applications using Python.

Who Should Attend

This course will help Python Programmers, Data Analysts and aspiring Data Science professionals.

Target Audiences

This course will help Python Programmers, Data Analysts and aspiring Data Science professionals.

Python is a versatile programming language. Many industries are now using Python for high-performance computing projects.

This course will teach you how to use Python on parallel architectures. You’ll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You’ll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you’ll construct robust and responsive apps using Reactive programming.

By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python.

About The Author

Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he was working as a Python developer at Qualcomm. He completed his Master’s degree in computer science from IIIT Delhi, with specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the higher-education industry.

Course Curriculum

Chapter 1: Getting Started with Faster and Efficient Python Code

Lecture 1: The Course Overview

Lecture 2: Exploring Python Datatypes

Lecture 3: Using Lambda Expressions

Lecture 4: Comprehensions for Speedups

Lecture 5: Generators and Iterators

Lecture 6: Using Decorators for Time Analysis

Chapter 2: Parallel Programming in Python

Lecture 1: Introduction to the Threading Module

Lecture 2: Using Threads with Locks

Lecture 3: Global Interpreter Lock

Lecture 4: Multiprocessing in Python

Lecture 5: Using a Pool of Workers

Chapter 3: Using NumPy and SciPy to Speedup Computations

Lecture 1: Introduction to NumPy

Lecture 2: Exploring NumPy Arrays

Lecture 3: Indexing in NumPy Arrays

Lecture 4: Operations and Broadcasting on NumPy Arrays

Lecture 5: Performance Comparison of NumPy Arrays

Lecture 6: Combining SciPy with NumPy

Chapter 4: Optimizing Python Code Using Cython

Lecture 1: Introduction to Cython

Lecture 2: Implement a Program Using Cython

Lecture 3: Time Analysis of a Cython Program

Lecture 4: Cython Data Types

Lecture 5: Using Cython Functions

Lecture 6: Combining NumPy and Cython

Chapter 5: Speeding Up Your Python Code Using Numba

Lecture 1: Introduction to Numba

Lecture 2: Setting Up Numba

Lecture 3: Creating Your First Program with Numba

Lecture 4: Digging Deeper into Numba

Lecture 5: Threading Using Numba

Lecture 6: Performance Comparison with Numba

Chapter 6: Distributed Computing Using Python

Lecture 1: Introduction to Synchronous Programming

Lecture 2: Understanding Asynchronous Programming

Lecture 3: Asynchronous Programming in Python

Lecture 4: Distributed Systems Architecture

Chapter 7: Distributed Programming Using Dask

Lecture 1: Introduction to Dask

Lecture 2: Setting Up Dask

Lecture 3: Blocked Algorithms and Dask Arrays

Lecture 4: Writing Your First Program Using Dask

Lecture 5: Using @delayed to Parallelize Code

Lecture 6: Performance Comparison with Dask

Chapter 8: Reactive Programming Using Python

Lecture 1: Introduction to Reactive Programming

Lecture 2: Observables and Observers

Lecture 3: Overview of Data Operators

Lecture 4: Reactive Programming in Python Using RxPy

Lecture 5: Using Data Operators with RxPy

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 14 votes
2 stars: 10 votes
3 stars: 30 votes
4 stars: 54 votes
5 stars: 50 votes

Frequently Asked Questions

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