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Learn to measure and optimize Python memory usage, exploring profilers for different scenarios like web apps and data processing to prevent crashes and improve performance.
Explore time series forecasting with Darts, a Python library unifying traditional and deep learning models. Learn best practices for preprocessing, backtesting, and achieving quick results in forecasting problems.
Explore how involvement in the Python community can boost personal and professional growth, enhance self-confidence, and provide valuable learning opportunities for tech enthusiasts.
Explore automatic testing of Python functions using contracts, reducing manual effort and improving code quality through tools like crosshair and icontract-hypothesis.
Aprenda a implementar detección de eventos sonoros con aprendizaje automático, desde la recopilación de datos hasta el entrenamiento de redes neuronales y la evaluación del sistema, utilizando Python y bibliotecas populares.
Explore data lake design strategies for managing schema evolution, focusing on storage, scalability, and AVRO format implementation. Learn best practices for maintaining data integrity and compatibility.
Explore Python 3.10's Structural Pattern Matching feature with its contributor. Learn motivations, problem-solving applications, and best practices for this powerful data processing tool.
Explore challenges and solutions in developing OctoPrint, an open-source web interface for 3D printers, focusing on end-user software maintenance and Python implementation insights.
Explore automated machine learning with Keras Tuner and AutoKeras, enabling efficient development of high-performance deep learning models while requiring less expertise and effort.
Explore next-generation tools for automating test case selection, test writing, and continuous testing. Learn about property-based testing, code introspection, and adaptive fuzzing to enhance your testing workflow.
Explore real-time RPC and PubSub over Websockets for distributed software, covering implementation challenges, use cases, and open-source solutions in Python.
Discover Python's magic through a personal project, exploring advanced concepts in a fun, engaging way. Suitable for beginners to professionals, with clear explanations and practical examples.
Explore causal inference techniques to uncover relationships in observational data, avoid misinterpretation pitfalls, and extract deeper insights beyond simple correlation calculations.
Explore dependency injection in Python: its concept, benefits, and implementation examples to enhance your engineering skills and code design.
Learn to transform a research project into a publicly released Python package, covering testing, CI, documentation, and citation on Zenodo. Ideal for researchers seeking to increase impact and visibility of their work.
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