Techlog 2022

Apr 2022

• https://www.elastic.co/guide/en/logstash/current/input-plugins.html
• https://cloud.netapp.com/blog/cvo-blg-elasticsearch-architecture-7-key-components
• For something in between a pytorch and a karpathy/micrograd This may not be the best deep learning framework, but it is a deep learning framework. The sub 1000 line core of it is in tinygrad/ - https://github.com/geohot/tinygrad
• A tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively. The DAG only operates over scalar values, so e.g. we chop up each neuron into all of its individual tiny adds and multiplies. However, this is enough to build up entire deep neural nets doing binary classification, as the demo notebook shows. Potentially useful for educational purposes. https://github.com/karpathy/micrograd
• Recruitathon - https://github.com/rexdivakar/Recruit-a-thon
• Ten Python projects with 10 lines of code - https://morioh.com/p/7ffbf70ad17a?f=5c21fb01c16e2556b555ab32
• Did you know that you can completely replace VBA in Excel with Python?
  • Script and automate Excel using nothing but Python, and write macro functions (Subs) and worksheet functions (UDFs) using PyXLL.
  • PyXLL https://www.pyxll.com​
  • Resources for VBA developers https://www.pyxll.com/learn-python
  • Subscribe: https://www.youtube.com/channel/UCfMaA6FC0aKYXEWubmOW5Vw
• pyshorteners is a Python lib to help you short and expand urls using the most famous URL Shorteners availables.- https://pyshorteners.readthedocs.io/en/latest/
• Parallel iteration with Python Zip - https://realpython.com/lessons/python-zip-function-overview/
• Write Python. Run anywhere. GUI development with Python - https://docs.beeware.org/en/latest/
• regex filters for various components of rss feeds. Useful for filtering:
  • Types of content (videos, audio)
    • Ads
    • Types of articles (summaries, links to other feeds, categories, etc)
    • Anything else you can find in the feed with a regex
  • https://github.com/bionoren/rssFilter
• Replacing Yahoo! Pipes with Python - https://gist.github.com/statico/1047390/aa8268ab5c5cfc7f0b1b1b6550b53c829ad8fcf4
• Current awareness tools - RSS and beyond https://researchguides.library.wisc.edu/c.php?g=864746&p=6201733
• Good Math - A Geek's Guide to the Beauty of Numbers, Logic, and Computation by Mark C. Chu-Carroll https://pragprog.com/titles/mcmath/good-math/
• https://sourceai.dev/documentation/introduction  - source code generation using GPT-3
• PaLM: Scaling Language Modeling with Pathways https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf
  • Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM). We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-ofthe-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned stateof-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.
• https://nlpcloud.io/nlp-chatbot-conversational-ai-gpt-j-api.html

Vim/Neovim: Scripting using Python

  alpha2phi  |    Tue, 15 Jun 2021 00:00:00 GMT  |    Time Saved: 1 mins

• Use Vimscript and Python in your development workflow.
• For Python developers, there is a simpler solution as both Vim and Neovim support using Python for plugin development.
• Run :checkhealth to see if you already have it.
• For Vim (:h python3), type :version to check if Vim is built with Python 3 support.
• Under your Vim/Neovim configuration folder, create a file called tts.vim under the autoload(:h autoload) folder.
• The source code I used can be found in this repository.
• https://sodocumentation.net/vim