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Visual Studio Code Must have Extensions

Hello World! Every programmer loves IDE and auto-complete inetllisense facilities in IDE's and editor tools. Since when Visual Studio Code came into market, it has attracted a lot of popularity among every age group of coders. But, any of the program doesn't suit to everyone's need, and thier comes the existence of extensions. Here I am listing out some of the very usefull extensions, that every Visual Studio Code user must have.

Hit Star if you liked this.

Just search the title in extensions marketplace.

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EMOJI CHEAT SHEET

Emoji emoticons listed on this page are supported on Campfire, GitHub, Basecamp, Redbooth, Trac, Flowdock, Sprint.ly, Kandan, Textbox.io, Kippt, Redmine, JabbR, Trello, Hall, plug.dj, Qiita, Zendesk, Ruby China, Grove, Idobata, NodeBB Forums, Slack, Streamup, OrganisedMinds, Hackpad, Cryptbin, Kato, Reportedly, Cheerful Ghost, IRCCloud, Dashcube, MyVideoGameList, Subrosa, Sococo, Quip, And Bang, Bonusly, Discourse, Ello, and Twemoji Awesome. However some of the emoji codes are not super easy to remember, so here is a little cheat sheet. ✈ Got flash enabled? Click the emoji code and it will be copied to your clipboard.

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Installing MySQL 8.0 Server with EntityFramework in Visual Studio 2017

• Install MySQL Server 8.0
• Install Complete MySQL Connector.Net x86 8.0.16
• Install Complete MySQL Visual Studio Plugin 1.2.8
• If after installing MySQL Visual Studio Plugin it shows warning that failed to execute comamnd devenv /updateconfiguration, then manually execute that command in Developer Command Prompt for Visual Studio 20xx with run as administrator.
• Run this command Install-Package EntityFramework -Version 6.4.0 in Visual Studio Nuget package manager console
• Run this command Install-Package MySql.Data -Version 8.0.16 in Visual Studio Nuget package manager console
• Run this command Install-Package MySql.Data.EntityFramework -Version 8.0.16 in Visual Studio Nuget package manager console

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import matplotlib.pyplot as plt
import keras.backend as K
from keras.callbacks import Callback


class LRFinder(Callback):
    
    '''
    A simple callback for finding the optimal learning rate range for your model + dataset. 
    

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"""Keras 1.0 metrics.

This file contains the precision, recall, and f1_score metrics which were
removed from Keras by commit: a56b1a55182acf061b1eb2e2c86b48193a0e88f7
"""
from keras import backend as K


def precision(y_true, y_pred):	
    """Precision metric.