From Project to Productionized on Heroku
Learn to deploy Django apps on Heroku: from development to production. Covers 12 Factor apps, configuration, collaboration, debugging, and essential Heroku tools for a smooth deployment process.
Python Concurrency - From Beginner to Pro
Comprehensive guide to Python concurrency, covering processes, threads, modules, race conditions, and the GIL. Includes coding examples and activities to build a multithreaded and multiprocessing web server.
Type Hints - Putting More Buzz in Your Fizz
Explore type hints in Python, from basics to complex structures. Learn debugging techniques, class definitions, and library stubs. Enhance code quality and confidence through practical examples.
Geospatial Public Policy Analysis with GeoPandas
Analyze US higher education institutions using GeoPandas, exploring education deserts and equity implications through hands-on data analysis with NCES and IPEDS datasets.
Datos, Python y Humanos
Explorando retos y oportunidades de la ciencia de datos en Latinoamérica: historias reales, obstáculos comunes y visión de futuro para científicos de datos y empresas en la región.
Education Summit - Bringing Coding Literacy Through Python
Educators discuss innovative approaches to teaching Python, including critical data science, context-based learning, and principles-driven development, to enhance coding literacy for diverse audiences.
Extending the Python Ecosystem to Xilinx
Explore PYNQ: Python-based platform integrating microprocessors, microcontrollers, and FPGAs. Learn to leverage programmable system-on-chips for embedded development using familiar Python ecosystem and Jupyter notebooks.
Polyglot Data with Python - Introducing Pandas and Apache Arrow
Explore polyglot data handling with Python using Pandas and Apache Arrow for efficient data exchange between ecosystems, improving performance and reducing file sizes.
Finite State Machine in Django
Explore Finite State Machines in Django for automating business processes, including workflow creation, audit logging, and visualization generation.
Deploying Python at Scale with Dask
Explore scaling Python with Dask on distributed systems, focusing on deployment challenges and solutions for cluster resource managers like Kubernetes, Yarn, and cloud platforms.
Introduction to Decorators - Power Up Your Python Code
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.
A Careful Walk Through Probability Distributions, Using Python
Explore probability distributions using Python, demystifying key concepts like sampling, joint, conditional, and marginal distributions for a solid foundation in Bayesian statistics.
Sorry, Could You Repeat That Again? - Speech Recognition with Python
Explore speech recognition systems and develop an isolated word recognition system in Python, with insights into real-world applications and advanced techniques.
GPU-Accelerated Data Analytics in Python
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.
How Python Powers Salesforce.org's Unique Open Source Model
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.
Getting Started with Automatic Differentiation
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.
We Need to Talk About Windows
Insider's look at building Python from source across platforms, focusing on Windows challenges and ActiveState's efforts to contribute to the Python community.
Magical NumPy with JAX
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.
Practical Deep Learning for Data Scientists
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.
Time for Time Series
Explore time series analysis fundamentals, decomposition techniques, and forecasting models using Pandas. Learn to extract insights and create powerful predictions from temporal data.
From Spreadsheets to DataFrames
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.