Deep Learning, NLP, NMT, AI, ML
AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks. Reinforcement learning (RL) is an approach where AI systems learn to make optimal decisions by receiving rewards or penalties for their actions, improving through
Read moreInteractive Quiz ⋅ 5 QuestionsBy Leodanis Pozo Ramos Share In this quiz, you’ll test your understanding of the Python Inner Functions: What Are They Good For? tutorial. By working through this quiz, you’ll revisit how inner functions work with enclosing scopes, when to use nonlocal to update captured state, how closures retain data across calls, and how decorators wrap a callable to extend behavior. You’ll apply these ideas to organize helpers, reuse state, and write clear, maintainable functions in real […]
Read moreAnyone who uses AI systems knows the frustration: a prompt is given, the response misses the mark, and the cycle repeats. This trial-and-error loop can feel unpredictable and discouraging. To address this, we are excited to introduce Promptions (prompt + options), a UI framework that helps developers build AI interfaces with more precise user control. Its simple design makes
Read moreThe convergence of digital transformation and the GenAI revolution creates an unprecedented opportunity for accelerating progress in precision health. Precision immunotherapy is a poster child for this transformation. Emerging technologies such as multiplex immunofluorescence (mIF) can assess internal states of individual cells along with their spatial locations, which is critical for deciphering how tumors interact
Read moreA lot happened last month in the world of Python! The core developers pushed ahead on Python 3.15, accepting PEP 810 to bring explicit lazy imports to the language. PyPI tightened account security, Django 6.0 landed with a slew of new features while celebrating twenty years of releases, and the Python Software Foundation (PSF) laid out its financial outlook and kicked off a year-end fundraiser. Let’s dive into the biggest Python news from the past month! Python Releases and PEP […]
Read moreInteractive Quiz ⋅ 7 QuestionsBy Bartosz Zaczyński Share In this quiz, you’ll test your understanding of the How to Use Google’s Gemini CLI for AI Code Assistance tutorial. By working through these questions, you’ll revisit how to install and verify prerequisites like Node.js, explore authentication options, and understand the CLI’s permission and safety model. You’ll also practice managing interactive sessions, enforcing stronger models, and approving or editing shell commands securely. To deepen your understanding, review the sections on verifying your […]
Read moreThis tutorial will teach you how to use Gemini CLI to bring Google’s AI-powered coding assistance directly into your terminal. After you authenticate with your Google account, this tool will be ready to help you analyze code, identify bugs, and suggest fixes—all without leaving your familiar development environment: Gemini CLI Imagine debugging code without switching between your console and browser, or picture getting instant explanations for unfamiliar projects. Like other command-line AI assistants, Google’s Gemini CLI brings AI-powered coding assistance […]
Read moreThe pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels. DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields. DataFrames are similar to SQL tables or the spreadsheets that you work with in Excel or Calc. In many cases, DataFrames are faster, easier to use, and more powerful than tables or spreadsheets because they’re an integral part of the Python and NumPy ecosystems. In this video course, you’ll learn: […]
Read moreHANNA WALLACH: Yeah, so I was a PhD student at the University of Cambridge, and I was working with the late David MacKay. I was focusing on machine learning for analyzing text, and at that point in time, I’d actually just begun working on Bayesian latent variable models for text analysis, and my research was really focusing on trying to combine ideas from n-gram language modeling with statistical topic modeling in order to come up with models that just did […]
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