Neuromorphic-Computing-Guide VS spaCy

Start reading papers on https://www.biorxiv.org/ and notice what seems most interesting or promising to you. Learn python. There are actually quite a few open source "into to machine learning" courses - maybe start with MIT's Learning Library, see what you find there. I also have this bookmarked for myself for later; I'm sure there are a few more goodies worth checking out here: https://github.com/mikeroyal/Neuromorphic-Computing-Guide

Tools and Resources for getting started with Neumorphic Computing. The process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

Neuromorphic engineering, which combines electrical, computer, and mechanical engineering with biology, physics, and neuroscience. uses specialized computing architectures that reflect the structure (morphology) of neural networks from the bottom up: dedicated processing units emulate the behavior of neurons directly in hardware, and a web of physical interconnections (bus-systems) facilitate the rapid exchange of information. Useful Tools and Resources for learning about Neuromorphic engineering.

Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks. Useful Tools and Resources for learning about Neuromorphic Computing.

Useful Tools and Resources for learning about Neuromorphic Computing. Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks.

UsefuleTools and Resource for about Neuromorphic Computing.

Neuromorphic Computing Guide/Wiki: https://github.com/mikeroyal/Neuromorphic-Computing-Guide

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.