
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 automatic differentiation in Python, its applications in optimization and computational art, using libraries like JAX, TensorFlow, and PyTorch. Gain intuition for derivatives and gradients.

Insider's look at building Python from source across platforms, focusing on Windows challenges and ActiveState's efforts to contribute to the Python community.

Explore JAX's NumPy-compatible API for performant numerical modeling and gradient-based optimization. Learn functional programming, deterministic random number generation, and neural network integration.

Hands-on PyTorch and GPU Deep Learning tutorial for data scientists. Build common architectures, evaluate performance, and leverage compute resources effectively. Ideal for those with ML basics seeking practical DL experience.

Explore time series analysis fundamentals, decomposition techniques, and forecasting models using Pandas. Learn to extract insights and create powerful predictions from temporal data.

Learn to transition from spreadsheets to Python DataFrames, exploring web scraping, data processing, analysis, and visualization using popular libraries like Pandas and Matplotlib. Gain skills for efficient data management and problem-solving.

Learn to create Python documentation using reStructuredText and Sphinx. Master essential skills for contributing to open source projects by improving their documentation and overcoming barriers to entry.

Learn to create, understand, and effectively use regular expressions in Python for data validation, parsing, and normalization. Discover when and how to apply regex for optimal results.

Learn to create interactive dashboards using Python libraries like Bokeh and Panel. Explore data visualization techniques, analyze diverse datasets, and tell compelling stories through customized, user-friendly interfaces.

Explore advanced Dask features: task graph optimization, plugins, cluster inspection. Gain deeper understanding of internals and apply to data-intensive workloads.

Learn to write high-quality Python unit tests using Pytest and mock. Master best practices for effective testing, enhancing code reliability and maintainability.

Explore functional programming concepts in Python, including higher-order functions, immutability, and lazy evaluation. Learn to write cleaner, more efficient code using functional paradigms.

Explore Instagram's Python optimization strategies, including runtime modifications, inline caching, and JIT compilation, achieving significant performance improvements for large-scale web applications.

Explore declarative configs for maintainable, reproducible code. Learn input formats, representations, deserialization, and type-safety. Discover strategies for evolving configs while preserving backwards compatibility.

Explore quantum computing in Python: access real quantum hardware, differentiate quantum programs, and integrate them into larger differentiable programs for optimization and training.

Explore static duck typing in Python using typing.Protocol, enhancing type hints for more expressive and verifiable code. Learn concepts and benefits through real-world examples from the standard library.

Explore FastAPI's features for rapid REST API development in Python, covering core concepts, schema creation, documentation, and advanced functionalities.

Explore Sphinx for building static websites using Markdown, leveraging its powerful features for rich linking and knowledge base creation beyond traditional documentation.

Explore Python's warning system for handling minor issues without raising exceptions. Learn to generate, filter, and customize warnings for more nuanced error handling in your code.

Explore Pydantic for runtime type checking in Python, enhancing data validation for web APIs using standard type annotations and offering compatibility with dataclasses.