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Discover techniques for elegant Python refactoring, transforming verbose code into concise, expressive solutions. Learn to identify anti-patterns and create more declarative implementations.
Demystifying Python list comprehensions: learn when, why, and how to use them effectively. Explore set and dictionary comprehensions, generator expressions, and best practices for readability and appropriate usage.
Explore Ray, an open-source framework for scaling Python applications from laptops to clusters, focusing on ML/AI performance challenges and its key features for distributed computing.
Explore doctests for embedding examples in docstrings, learn about Xdoctest as an alternative to Python's builtin module, and discover how to enhance test-driven development with interactive code demos.
Uncover the inner workings of deep learning frameworks and gain foundational knowledge to confidently implement models in any framework of your choice.
Aprenda a traduzir aplicativos Django para vários idiomas, incluindo detecção de idioma, roteamento de URL, arquivos de tradução e marcação de strings. Inclui dicas para traduzir o site de administração do Django.
Experiencias de depuración en clases de Python avanzado: cuatro casos intrigantes y sus soluciones, compartidos por un instructor de la Universidad de Washington.
Explore how Python and open source tools drive Salesforce.org's development, enabling community-built solutions for nonprofits and education. Learn to integrate Python apps with Salesforce's CRM platform.
Explore Python's performance, comparing it to other languages, and learn optimization techniques and future improvements for faster execution.
Explore GPU-accelerated data analytics using RAPIDS libraries in Python. Learn ETL, machine learning, graph analytics, and more with familiar APIs for faster computation and model iteration.
Explore speech recognition systems and develop an isolated word recognition system in Python, with insights into real-world applications and advanced techniques.
Explore probability distributions using Python, demystifying key concepts like sampling, joint, conditional, and marginal distributions for a solid foundation in Bayesian statistics.
Learn strategies for migrating Python 2 code to Python 3, focusing on writing single-source programs compatible with both versions using python-future. Gain practical skills for a smooth transition.
Explore Python decorators: enhance code functionality, improve efficiency, and add capabilities with ease. Learn to create and apply decorators for more powerful and flexible programming.
Explore scaling Python with Dask on distributed systems, focusing on deployment challenges and solutions for cluster resource managers like Kubernetes, Yarn, and cloud platforms.
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