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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 JAX's NumPy-compatible API for performant numerical modeling and gradient-based optimization. Learn functional programming, deterministic random number generation, and neural network integration.
Learn essential Git concepts and commands for effective collaboration in software development projects, including cloning, inspecting, and comparing repositories.
Explore alternatives to mocks in testing, focusing on design patterns and techniques for better code structure, maintainable tests, and a healthy test pyramid.
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 phonetic analysis and word generation using Python and machine learning. Learn to create nonsense verse and analyze word sounds with the Pincelate library.
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.
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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