GitHub

An example of a LSTM network implementation to generate text on a word-for-word basis using tensorflow and keras. Based on a keras example.

The current example is trained on Alice in Wonderland, and ready to go. Use lstm_generate.py to generate runs of 400 words of varying diversities. You can provide new full-text input data in the root ...
Aalto University

Checklist for the accessibility of learning material and digital content

This site provides you with a comprehensive checklist to help you ensure that your learning materials are accessible to all ...
Investopedia

Financial Statements: List of Types and How to Read Them

Meagan is a former Series 7 financial advisor and current writer focused on blending straightforward information with a dose of humor on topics including equity investments, insurance products, and ...
IEEE

Query-by-Example Speech Search Using Recurrent Neural Acoustic Word Embeddings With Temporal Context

Abstract: Acoustic word embeddings (AWEs) have been popular in low-resource query-by-example speech search. They are using vector distances to find the spoken query in search content, which has much ...
GitHub

thomasloven/lovelace-auto-entities

Automatically populate lovelace cards with entities matching certain criteria. Install Auto Entities through HACS or see this guide.
Reader's Digest

15 of the Hardest Words to Spell in the English Language

The English language is full of words that seem overstuffed with unnecessary letters, feel like they should be spelled a different way, or just don't make sense. Here are some of our favorites, ...