Quiz: Use TorchAudio to Prepare Audio Data for Deep Learning

Interactive Quiz ⋅ 10 QuestionsBy Bartosz Zaczyński Share In this quiz, you’ll test your understanding of audio fundamentals and how to Use TorchAudio to Prepare Audio Data for Deep Learning. You’ll revisit fundamental terminology and how to: Install and import TorchAudio Load audio waveform datasets Apply signal transforms Work through these questions to check your knowledge about building audio workflows for machine learning in Python. The quiz contains 10 questions and there is no time limit. You’ll get 1 point […]

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Use TorchAudio to Prepare Audio Data for Deep Learning

Ever wondered how machine learning models process audio data? How do you handle different audio lengths, convert sound frequencies into learnable patterns, and make sure your model is robust? This tutorial will show you how to handle audio data using TorchAudio, a PyTorch-based toolkit. You’ll work with real speech data to learn essential techniques like converting waveforms to spectrograms, standardizing audio lengths, and adding controlled noise to build machine and deep learning models. Dive into the tutorial to explore these […]

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Quiz: The Python print() Function

Interactive Quiz ⋅ 12 QuestionsBy Bartosz Zaczyński Share In this quiz, you’ll test your understanding of Your Guide to the Python print() Function. The print() function outputs objects to the console or a specified file-like stream. You’ll practice: Printing multiple values with custom separators Changing the end-of-line character Redirecting output using the file parameter Forcing immediate output with the flush parameter Work through these questions to reinforce your knowledge of print()’s parameters and best practices for clear, formatted I/O. The […]

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Your Guide to the Python print() Function

If you’re like most Python users, then you probably started your Python journey by learning about print(). It helped you write your very own “Hello, World!” one-liner and brought your code to life on the screen. Beyond that, you can use it to format messages and even find some bugs. But if you think that’s all there is to know about Python’s print() function, then you’re missing out on a lot! Keep reading to take full advantage of this seemingly […]

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Starting With DuckDB and Python

The DuckDB database provides a seamless way to handle large datasets in Python with Online Analytical Processing (OLAP) optimization. You can create databases, verify data imports, and perform efficient data queries using both SQL and DuckDB’s Python API. By the end of this video course, you’ll understand that: You can create a DuckDB database by reading data from files like Parquet, CSV, or JSON and saving it to a table. You query a DuckDB database using standard SQL syntax within […]

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Python enumerate(): Simplify Loops That Need Counters

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Looping With Python enumerate() Python’s enumerate() function helps you with loops that require a counter by adding an index to each item in an iterable. This is particularly useful when you need both the index and value while iterating, such as listing items with their positions. You can also customize the starting index with […]

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AI Testing and Evaluation: Learnings from Science and Industry

[MUSIC ENDS]   For our introductory episode, I’m pleased to welcome Amanda Craig Deckard from Microsoft to discuss the company’s efforts to learn about testing in other sectors.   Amanda is senior director of public policy in the Office of Responsible AI, where she leads a team that works closely with engineers, researchers, and policy experts to help ensure AI is being developed and used responsibly. Their insights shape Microsoft’s contribution to public policy discussions on laws, norms, and standards for AI.   […]

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Learning from other domains to advance AI evaluation and testing

As generative AI becomes more capable and widely deployed, familiar questions from the governance of other transformative technologies have resurfaced. Which opportunities, capabilities, risks, and impacts should be evaluated? Who should conduct evaluations, and at what stages of the technology lifecycle? What tests or measurements should be used? And how can we know if the results are reliable?   Recent research and reports from Microsoft (opens in new tab), the

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AI Consulting

Kavita Ganesan is the author of the Amazon bestselling book, The Business Case for AI and founder of Opinosis Analytics, an AI consulting company providing advisory, strategy and development services. With over 15 years of experience in the field, Kavita has scaled and delivered multiple successful AI initiatives for large companies such as eBay, 3M, and GitHub, as well as smaller organizations. Kavita holds advanced degrees from prestigious computer science programs, specifically a Masters’s from the University of Southern California […]

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