Modin is an early-stage project at UC Berkeley’s RISELab designed to facilitate the use of distributed computing for Data Science.
It is a multiprocess Dataframe library with an identical API to pandas that allows users to speed up their Pandas workflows.
Link
Given about 7000 experimental datapoints, we want to understand which parameters influence the metric we want to optimize: valid ppl. How can HiPlot help?
On the parallel plot, each line represents one datapoint. Slicing on the valid ppl axis reveals that higher values for lr lead to better models.
We will focus on higher values for the lr then. Un-slice the valid ppl axis by clicking on the axis, but outside of the current slice.
Slack chats can become messy with time, proving difficult to extract meaningful information.
In this article, I want to present a quick codeless way of fine-tuning and deploying the commonly used BERT classifier to do conversational analysis.
We will use that system to extract tasks, facts, and other valuable information from our Slack conversations.
It could be easily extended for categorizing any other textual data, like support requests, emails, etc.
Loguru is a library which aims to bring enjoyable logging in Python.
Did you ever feel lazy about configuring a logger and used print() instead?… I did, yet logging is fundamental to every application and eases the process of debugging. Using Loguru you have no excuse not to use logging from the start, this is as simple as from loguru import logger.
Also, this library is intended to make Python logging less painful by adding a bunch of useful functionalities that solve caveats of the standard loggers.
It is a Javascript based diagramming and charting tool that renders Markdown-inspired text definitions to create and modify diagrams dynamically
Link
mermaid-live-editor
Python Integration with mermaid-js
Example:
sequenceDiagram
participant Alice
participant Bob
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts <br/>prevail!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
Build and Deploy Data Science Products : A Practical Guide to Building a Machine Translation Application This journey is going to be a 8 steps.
In this series we will take a use case, understand the solution landscape and its evolution, explore different architecture choices, look under the hood of the architecture to understand the nuts and bolts, build a prototype, convert the prototype into production ready code, build an application from the production ready code and finally understand the process for deploying the application .
This project demonstrates the Udacity self-driving-car dataset and YOLO object detection into an interactive Streamlit app.
The complete demo is implemented in less than 300 lines of Python and illustrates all the major building blocks of Streamlit.
Link
Introduced in the paper, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, Vision Transformers (ViT) are the new talk of the town for SOTA image classification.
Experts feel this is only the tip of the iceberg when it comes to Transformer architectures replacing their convolutional counterparts for upstream/downstream tasks.
Link
