Jupyter Reviews and details

Social recommendations and mentions

• The 3 Best Python Frameworks To Build UIs for AI Apps

  Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago

• LangChain: From Chains to Threads

  LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago

• Applied Artificial Intelligence & its role in an AGI World

  Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago

• Jupyter Notebook for Java

  Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago

• JIRA Analytics with Pandas

  One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago

• How to install Python in Windows 11?

  Keep in mind that Python has a vibrant ecosystem of libraries and tools. You can use a code editor or integrated development environment (IDE) like Visual Studio Code, PyCharm or Jupyter Notebook to write and run Python code more effectively. - Source: dev.to / 9 months ago

• Understanding Your Data: The Essentials of Exploratory Data Analysis

  Jupyter Notebooks This is an interactive environment for running and saving Python code in a step-by-step manner. It is commonly used in the data space because it provides a flexible environment to work with code and data. For more on Jupyter notebooks click here. - Source: dev.to / 9 months ago

• How to turn a Jupyter Notebook into a deployable artifact

  A Jupyter Notebook is a web-based interactive tool that allows you to create a computational environment to produce documents containing code and rich text elements. This is the standard tool for research and development of a new machine learning model or a new fine-tuning methodology because Jupyter Notebook is focused on:. - Source: dev.to / 10 months ago

• The Gemika's Magical Guide to Sorting Hogwarts Students using the Decision Tree Algorithm (Part #2)

  Just as a wizard requires a wand, a data scientist requires Python to cast their spells. Let’s gather around the cauldron and brew a potion of installations, setting up Python and Jupyter Notebook, which will be our magical companions in this adventure. 🪄✨. - Source: dev.to / 11 months ago

• The Gemika's Magical Guide to Sorting Hogwarts Students using the Decision Tree Algorithm (Part #1)

  Professor Nugroho, a close confidant of the venerable Albus Dumbledore, has dedicated his life to unraveling the mysteries of both magic and data. With a wand in one hand and a Jupyter Notebook on the other, he delves into the secrets of the magical universe. His office, tucked away in a quiet corner of Hogwarts, is a haven of books and scrolls, with enchanted quills scribbling notes and cauldrons bubbling with... - Source: dev.to / 11 months ago

• Interactive Visualizations of Rate-Limiting Algorithms

  Interesting, I would have guessed you had used something jupyter-like: https://jupyter.org/ https://explorabl.es/all/. - Source: Hacker News / 12 months ago

• scrape-yahoo-finance

  JupyterLab: JupyterLab is an interactive development environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's particularly well-suited for data science and research-oriented projects. - Source: dev.to / about 1 year ago

• Let’s build AI-tools with the help of AI and Typescript!

  Jupyter Lab web-based interactive development environment. - Source: dev.to / about 1 year ago

• Scrape Redfin Property Data

  Choosing IDE: Selecting a suitable Integrated Development Environment (IDE) is crucial for efficient coding. Consider popular options such as PyCharm, Visual Studio Code, or Jupyter Notebook. Install your preferred IDE and ensure it's configured to work with Python. - Source: dev.to / about 1 year ago

• Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy

  Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / about 1 year ago

• Using IPython Jupyter Magic commands to improve the notebook experience

  Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / about 1 year ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  They make it easy to launch multiple case-by-case data science projects and run your local code right from Jupyter Notebook. - Source: dev.to / over 1 year ago

• MLOps in practice: building and deploying a machine learning app

  Talking to some colleagues and friends lately gathering some ideas of a nice Machine Learning project to build, I’ve seen that there’s a gap of knowledge in terms of how do one exactly uses a Machine Learning model trained? Just imagine yourself building a model to solve some problem, you are probably using Jupyter Notebook to perform some data clean up, perform some normalization and further tests. Then you... - Source: dev.to / over 1 year ago

• Stuff I Learned during Hanukkah of Data 2023

  This year I decided to commit to a set of tools on day 1 (Polars and Jupyter) and use them for the whole challenge. It seemed silly to do a whole new meandering walkthrough, so instead I'll highlight a few things that stuck out after finishing the challenge and sitting on it for a few days. Here we go! - Source: dev.to / over 1 year ago

• Technical reports generation with Raku

  The resulting technical reports can be in the formats: Markdown, Pod6, or Org-mode. Or just Jupyter notebooks. Source: over 1 year ago

• No comments. Now what?

  Another effective way to use comments is through literate programming. In this programming style, comments take the spotlight: the source code contains more prose than executable code. This is useful when explaining the algorithm is more important than reading it, as in academic research and data analysis. Not surprisingly, it is the paradigm of popular tools like Jupyter Notebook and Quarto. - Source: dev.to / over 1 year ago