Packt – Python for Penetration Testers-QUiD

Packt – Python for Penetration Testers-QUiD
English | Size: 712.07 MB
Category: Tutoial


Develop your Python skills to get started with penetration testing and cybersecurity
This book is a practical guide that shows you the advantages of using Python for pentesting with the help of detailed code examples.

Udemy – Asynchronous Programming with Python – Write, Test, and Debug Robust Asynchronous Code

Udemy – Asynchronous Programming with Python – Write, Test, and Debug Robust Asynchronous Code
English | Size: 235.31 MB
Category: Tutorial


In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, and how to use it risk-free. Further, it covers Python’s global interpreter lock, which prevents a lot of serious problems in Python but also stops you from running threads in parallel.

O’Reilly – Applied Computer Vision with Python Video Course

O’Reilly – Applied Computer Vision with Python Video Course
English | Size: 2.73 GB
Category: CBTs


Get started with Applied Computer Vision in Python. Topics include: 1. Introduction to applied computer vision 2. Emerging Topics in applied computer vision 3. Using AI APIs 4. Using AutoML for Computer vision 5. Using Edge Computer Vision Hardware 6. Using AWS for Computer Vision with AWS DeepLens and AWS Lambda

Cloud Computing with Python Video Course

Cloud Computing with Python Video Course
English | Size: 3.04 GB
Category: Tutorial


Get started with Cloud Computing in Python. Topics include: Multi-Cloud, Cloud Computing Service Models, Distributed Computing in the Cloud, Cloud Computing ETL Pipelines, Serverless Soutions with the Cloud, Containers in the Cloud, and Continuous Delivery for ML Engineers