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Philip Vollet Profile picture
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16 May, 4 tweets, 2 min read
Note taking apps are like muscle training - you have to do it every day.

How many times I have changed ...

From Evernote to OneNote to Google Keep to Notion and from Roam now to Obsidian

@obsdmd

Why?
Where the big ones like OneNote, Google Keep and Evernote fail is that the brain does not work like an index, thoughts are linked and associatively this is where the next generation of note taking apps show their strength.

Roam and Obsidian

roamresearch.com
Map your notes and thoughts into a graph and weave them together.

What bothered me about Roam is that it doesn't have a native client and only runs in the browser, and this is where Obsidian comes in!
It has the strengths of Roam and adds more.

Native clients and a strong extension ecosystem with many active developers!

Pure makrdown with graph power!

obsidian.md

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More from @philipvollet

Philip Vollet Profile picture
15 May
Your open source project is ready for deployment? Documentation is still missing?

Good documentation and its presentation is an art!

A case study with 4 examples on awesome documentation
What makes good documentation?

- No prosaic texts! Choose a practical approach with code snippets
- Good structure and overview with a quick entry then in depth
- Good search is everything
- Good code examples
A superbly executed documentation is the one by @explosion_ai about @spacy_io

Why?
-Extremely good search
-These diagrams eye candy everywhere!
-Interactivity
-Live code examples that can be customized and run in a Binder container

spacy.io
Read 14 tweets
Philip Vollet Profile picture
13 May
Where to get data for your next machine learning project?

An overview of 8 amazing resources to accelerate your next project with data!

- Google Datasets
- Big Bad NLP Datasets
- Hugging Face Datasets
- Papers with Code Datasets
- Open Data on AWS
- Awesome Public Datasets
Hugging Face Datasets

Mainly for NLP but the good news Hugging Face is expanding and we can be sure that they will add datasets for visual machine learning soon!

@huggingface

huggingface.co/datasets
Big Bad NLP Datasets

One of the best sources for sophisticated Natural Language Processing datasets

@Quantum_Stat

datasets.quantumstat.com
Read 11 tweets
Philip Vollet Profile picture
11 May
How to get your dream job in Data Science if you are a career changer?

First you have to sneak around HR and their antiquated methods. This is only possible through contacts or unusual ways.

But what are good ways?
The middleman

Someone who can hand over your application who has a connection to the company or someone who works there.
The direct way, but be careful this must be done well

You look for a contact person via Linkedin, but the pitch has to be right and you really have to have an interesting application. Otherwise it looks like spam and you are out of the game forever.

How?
Read 6 tweets
Philip Vollet Profile picture
10 May
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? • Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks.

Paper arxiv.org/abs/2104.07762
GitHub

↓ 1/4
github.com/elehman16/expo…

The cost of training such models and the necessity of data access to do so is coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT.

↓ 2/4
While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar).

Would it be safe to release the weights of such a model if they did?

↓ 3/4
Read 4 tweets
Philip Vollet Profile picture
8 May
To build a chatbot you need data for your intent classification.

But what if you have too little training data?

Paraphrasing is one option for augmentation

But what is a good paraphrase? Image
Almost all conditioned text generation models are validated on 2 factors:

1. If the generated text conveys the same meaning as the original context (Adequacy)

2. If the text is fluent / grammatically correct english (Fluency)
For instance Neural Machine Translation outputs are tested for Adequacy and Fluency.
Read 11 tweets
Philip Vollet Profile picture
7 May
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
Read 8 tweets

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