Philip Vollet Profile picture
May 7, 2021 8 tweets 5 min read Read on X
How do you create a beautiful interface for your machine learning or data science project?

Handmade from scratch?
Any good tools?

Sure there are incredible tools:
Beautiful ML & DS interfaces

Gradio
Quickly create customizable UI components around your ML models. By dragging-and-dropping in your own images, pasting your own text, recording your own voice & seeing what the model outputs.

@GradioML

github.com/gradio-app/gra…
Beautiful ML & DS interfaces

Dash apps bring Python analytics to everyone with a point-&-click interface to models written in Python, R & Julia - vastly expanding the notion of what's possible in a traditional dashboard.

@plotlygraphs

plotly.com/dash
Beautiful ML & DS interfaces

Panel a high-level app and dashboarding solution for Python

Check awesome-panel.org/gallery for inspiration

@MarcSkovMadsen

github.com/holoviz/panel
Beautiful ML & DS interfaces

H2O Wave is an open-source Python development framework that makes it fast and easy for data scientists, machine learning engineers, and software developers to develop real-time interactive AI apps.

h2o.ai/products/h2o-w…
Beautiful machine learning interfaces

Streamlit
The fastest way to build and share data apps. Streamlit turns data scripts into shareable web apps in minutes.
All in Python. All for free. No front‑end experience required.

@streamlit

streamlit.io
Beautiful ML & DS interfaces

Pandas-Bokeh provides a plotting backend for Pandas, GeoPandas and Pyspark DataFrames

Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series.

@bokeh

$ pip install pa.ndas-bokeh

github.com/PatrikHlobil/P…

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More from @philipvollet

Jul 27, 2021
Tools that make every data scientist and machine learning engineer better!

The universal swiss army knife infrastructure and scheduling collection:
Redis as a data structures server for caching queue handling

Redis provides access to mutable data structures via a set of commands!

github.com/redis/redis
Celery - Distributed Task Queue with Redis

It’s a task queue with focus on real-time processing, while also supporting task scheduling

Docs docs.celeryproject.org/en/stable/inde…

github.com/celery/celery
Read 9 tweets
Jun 11, 2021
Insights from an open source influencer

I'm often asked how I get my content, over the years I've built an unusual technology stack for it

philipvollet.co

Some insights:
I use Feedly for most content inputs because I can access the content through a single API endpoint and scraping is often pure pain.

@feedly

Feedly saves me a lot of time and manual work.

feedly.com
To pull and enrich my GitHub content I use ghapi from @fastdotai which provides a 100% always-updated coverage of the entire GitHub API

ghapi.fast.ai
Read 8 tweets
Jun 11, 2021
XBNet: An Extremely Boosted Neural Network for Tabular Data with a novel architecture combining tree-based models with neural networks

arxiv.org/abs/2106.05239

github.com/tusharsarkar3/…
Trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.
This tweet is a collaboration with Tushar Sarkar so why not follow for first hand updates?

linkedin.com/in/tushar-sark…
Read 4 tweets
Jun 11, 2021
Reconstructing Implicit Knowledge with Language Models.

Generating statements that explicate implicit knowledge connecting sentences in text.

aclweb.org/anthology/2021…

github.com/Heidelberg-NLP…
They make use of pre-trained language models which they refine by fine-tuning them on specifically prepared corpora that we enriched with implicit information and by constraining them with relevant concepts and connecting commonsense knowledge paths.
Manual and automatic eva. of the generations shows that by refining language models as proposed they can generate coherent & grammatically sound sentences that explicate implicit knowledge which connects sentence pairs in texts on both in-domain and out-of-domain test data
Read 4 tweets
Jun 10, 2021
Deepface is a lightweight face recognition and facial attribute analysis framework in Python

$ pip install deepface

@serengil

Don't forget to spend some star love for the repository!

github.com/serengil/deepf…
It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib

The library is mainly based on Keras & TensorFlow
This tweet is a collaboration with Sefik Ilkin Serengil so why not follow for first hand updates?

@serengil

Feel free to ask anything in the comments!
Read 4 tweets
Jun 10, 2021
Quant UX is a research, usability and prototyping tool to quickly test your designs & get data driven insights

@quant_ux

Quant-UX makes it simple to validate your ideas. Create a prototype, share a link & learn through user feedback and analytics

quant-ux.com
This tweet is a collaboration with Klaus Schaefers why not follow for first hand updates?

Feel free to ask anything!

linkedin.com/in/klaus-schae…
Read 4 tweets

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