artificial-intelligence

🐛 Bug

tuner.scale_batch_size finds the suitable batch size and update the batch size of the model AND datamodule.
For the model, tuner.scale_batch_size updates the batch size in the model regardless of model.batch_size and model.hparams.batch_size.

However, for the datamodule, tuner.scale_batch_size updates datamodule.batch_size only, and keep datamodule.hparams.batch_size

At this moment relu_layer op doesn't allow threshold configuration, and legacy RELU op allows that.
We should add configuration option to relu_layer.

Hey Hackers of this spoopy month! 👻
Welcome to the Ciphey repo(s)!
This issue requires you to add a decoder.

This wiki section walks you through EVERYTHING you need to know, and we've added some more links at the bottom of this issue to detail more about the decoder.
https://github.com/Ciphey/Ciphey/wiki#adding-your-own-crackers--decoders

https://www.dcode.fr/t9-cipher
https://en.wikipe

Currently, when entering epic mode the README is frozen in the last level of the tower. When you're trying to fine-tune the score for a level other than the last one, it would be helpful if we had the README for that level available. The proposal is that when entering epic mode, the README is updated with all levels, one following the other.

Example:

# Starbolt - beginner

Specs

• Leon version: latest
• OS (or browser) version: Fedora 30
• Node.js version: 10.16.3
• Complete "npm run check" output:

➡ Here is the diagnosis about your current setup
✔ Run
✔ Run modules
✔ Reply you by texting
❗ Amazon Polly text-to-speech
❗ Google Cloud text-to-speech
❗ Watson text-to-speech
❗ Offline text-to-speech
❗ Google Cloud speech-to-text
❗ Watson spee

Introduction

This tutorial should go over training a model using MindsDB that predicts whether an email is Spam or not. Example dataset can be found here.

Tutorial summary

The tutorial should follow the below steps :

• Connect mindsb to database
• Train new model
• Desc