Python
The language itself: core features, data structures, OOP, testing, performance and the standard library.
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- Unlocking Metaclass Power in Python6 min
Metaclasses are a powerful feature in Python that allow you to customize class creation. They provide a way to intercept and modify the class creation...
- Unlocking Python Classes and Objects8 min
In Python, classes and objects are fundamental concepts of object-oriented programming (OOP). A class is a blueprint for creating objects, while an object is...
- Using @override to Ensure Proper Method Overriding in Python4 min
The @override decorator is a built-in feature introduced in Python 3.12 that helps catch bugs by ensuring methods intended to override superclass methods...
- 3 Approaches to Concurrency in Python6 min
Concurrency in Python allows multiple tasks to run seemingly simultaneously, improving performance and efficiency. This slideshow explores three main...
- Accelerating Python with Numba9 min
Numba is a just-in-time compiler for Python that can significantly speed up numerical and scientific Python code. It works by translating Python functions to...
- Avoiding Persistent Objects with Reference Cycles9 min
Reference cycles occur when two or more objects contain references to each other, creating a circular dependency that prevents Python's reference counting...
- Concurrency and Parallelism in Python Slideshow Guide7 min
Introduction to Concurrency and Parallelism
- CUDA Programming with Python Slideshow8 min
CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical...
- Disabling GIL in Python 3.13 for Faster Multi-Threading4 min
Python 3.13 introduces a significant change in how it handles multi-threading. The Global Interpreter Lock (GIL), a mechanism that prevents multiple native...
- Disabling the Python Global Interpreter Lock (GIL)5 min
The Global Interpreter Lock (GIL) is a mechanism used in CPython, the reference implementation of Python, to synchronize thread execution. It ensures that...
- Efficient Asynchronous Socket Programming with asyncio in Python11 min
Understanding asynchronous socket programming requires grasping how asyncio manages non-blocking I/O operations. The asyncio library provides high-level APIs...
- Mastering Python Memory Management! Profiling and Optimization4 min
Python's memory management is automatic, but understanding its intricacies can help optimize your code. This presentation will cover profiling techniques and...
- Mastering Python's Memory Management7 min
Python's memory management is a crucial aspect of the language that often goes unnoticed by developers. It involves two main techniques: reference counting...
- Memory-Efficient Duplicate Removal in Python7 min
Memory efficiency in duplicate removal operations is critical when dealing with large datasets in Python. The choice of method can significantly impact both...
- Mini-Batching with In-Memory Datasets in Python10 min
Mini-batching is a technique used in machine learning to process subsets of data during training. It allows for efficient computation and improved...
- Optimize Python Memory Usage with memory_profiler8 min
Understanding memory consumption in Python applications is crucial for optimization. The memory\profiler module provides detailed insights into memory usage...
- Optimizing Python with Cython! A Slideshow6 min
Cython is a powerful tool for optimizing Python code by converting it to C, resulting in significant performance improvements. It allows developers to write...
- Parallel Processing with Python ProcessPoolExecutor7 min
ProcessPoolExecutor is a powerful tool in Python's concurrent.futures module. It allows you to parallelize CPU-bound tasks by utilizing multiple processes....
- Profiling Python Code Execution with NeoLogger's Stopwatch4 min
The Stopwatch class provides precise timing functionality for measuring code execution durations, supporting both cumulative and lap timing modes with...
- Python 3.13's JIT Compiler A Performance Boost4 min
Python 3.13 does not currently exist, and there are no official plans for a JIT compiler in Python's core implementation. The latest stable version of Python...
- Python Prefetching for Efficient Handling of Huge Data Sets4 min
Prefetching is a technique used to optimize data access by loading data into memory before it's actually needed. This can significantly improve performance...
- Speeding Up Python Programs with Short-Circuit Evaluation6 min
Short-circuit evaluation is a fundamental optimization technique in programming where the second argument of a logical operation is only evaluated if the...
- Three Concurrency Techniques in Python7 min
Concurrency in Python allows multiple tasks to run simultaneously, improving efficiency and performance. This presentation explores three primary methods...
- Timing Python Code with timeit5 min
The timeit module in Python is a powerful tool for measuring the execution time of small code snippets. It provides an easy and accurate way to benchmark...
- Top Multithreading Design Patterns in Python16 min
The Producer-Consumer pattern represents a classic synchronization problem where producers generate data and consumers process it using a shared buffer. This...
- Understanding Python Memory Leaks4 min
Memory leaks in Python are indeed possible, despite the presence of a garbage collector. While Python's automatic memory management helps prevent many common...
- Weak References in Python! Memory Management and Practical Uses8 min
Weak references provide a way to refer to objects without increasing their reference count. This allows for more flexible memory management and can help...
- 5 Powerful Ways to Use Underscores in Python3 min
The underscore (\) in Python is a versatile symbol with multiple uses. This presentation focuses on its role as a throwaway or placeholder variable, a...
- 9 Essential Python Command Line Flags6 min
The -m flag allows executing modules as scripts directly from the command line, enabling clean module imports and proper package handling. This approach is...
- Adam Optimizer in Python12 min
Adam (Adaptive Moment Estimation) is an optimization algorithm used in training deep learning models. It combines the benefits of two other extensions of...