hal9ai
examples
Our great sponsors
hal9ai | examples | |
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22 | 143 | |
122 | 7,742 | |
- | 1.2% | |
-22.7 | 6.2 | |
9 months ago | 22 days ago | |
TypeScript | Jupyter Notebook | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hal9ai
- Show HN: Will data apps into existence with GPT-3
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PyScript
We are not using libfortran not gdpr, we are basically using whatever libraries are available for the web. Since most data scientists don't want to use JS per se, you can build the apps as blocks in the Hal9 site or using a soon-to-be-released Python/R package, see https://notebooks.hal9.com
Feel free to check out our repo as well, all the "primitives" / blocks code is in the scripts folder: https://github.com/hal9ai/hal9ai
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Ask HN: Can you share websites that are pushing the utility of browsers forward?
https://hal9.com helps data scientists build faster web applications.
It uses WebGL and WebAssembly to process larger datasets, perform inference in the browser with TensorFlow.js, and enables running Python code with Pyodide.
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Ask HN: What ML platform are you using?
If you want to build a web application on top of your ML project, give https://hal9.com a shot. We designed Hal9 with ease of use for deployment and maximum compatibility with web technologies that enable you to build ML apps with React, Vue, etc. We launched a couple months ago but could use some early feedback and users. Thank you!
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Built data analysis platform optimized for web developers
BTW. If you are ever interested in helping us out, you can send a PR's to our GitHub repo. For instance, the summarize and convert blocks are here: https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/summarize.txt.js and https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/convert.txt.js
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PyFlow β visual and modular block programming in Python
We are working in https://hal9.com which is language agnostic and allows you to compose different programming languages; however, we are focused at the moment at 1D-graphs but have plans to support 2D-graphs in the coming weeks.
If you want a demo or just time to chat, I'm available at javier at hal9.ai.
- hal9ai: Web-First Composable Data Pipelines
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Mlflow, fastapi, streamlit template Project
We would love to help out since this is a perfect use case for https://hal9.ai; we are about to release our beta version that makes this as easy as copy-pasting code. You can find me at javier at hal9.ai to find some time to chat and give you a walkthrough of our code-to-api functionality.
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Flask with react.js
Hi there, would love to help you out. We are building a JavaScript + Python/R/AI service called https://hal9.ai to enable you to build React/Vue/Svelte/Angular apps against Python/R models. Want to send me an email to javier at hal9.ai to chat and help out?
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Is BI dead? β On dismantling data's ship of Theseus
Would love to hear your feedback for https://hal9.ai -- We are building an open source platform for data analysis based on reusable code blocks and a community to build and monetize their contributions. We are pretty early in our journey, launched our alpha and getting ready for our beta release, but would love to hear your thoughts. You can find me at javier at hal9.ai. Cheers!
examples
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Noviceβs Guide π
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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π₯14 Excellent Open-source Projects for Developersπ
10. TensorFlow - Make Machine Learning Work for You π€
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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π₯π Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot π€π¬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
What are some alternatives?
arquero - Query processing and transformation of array-backed data tables.
cppflow - Run TensorFlow models in C++ without installation and without Bazel
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
mlpack - mlpack: a fast, header-only C++ machine learning library
blockly - The web-based visual programming editor.
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
starboard-notebook - In-browser literate notebooks
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
ml5-library - Friendly machine learning for the web! π€
Selenium WebDriver - A browser automation framework and ecosystem.
gradio - Build and share delightful machine learning apps, all in Python. π Star to support our work!
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing