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// Place your key bindings in this file to override the defaults | |
[ | |
// Toggle between terminal and editor focus | |
{ | |
"key": "ctrl+`", | |
"command": "workbench.action.terminal.focus" | |
}, | |
{ | |
"key": "ctrl+`", | |
"command": "workbench.action.focusActiveEditorGroup", |
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import tweepy | |
from dotenv import load_dotenv | |
import os | |
# Constants | |
load_dotenv() | |
#Secrets | |
CONSUMER_KEY = os.getenv("CONSUMER_KEY") | |
CONSUMER_SECRET = os.getenv("CONSUMER_SECRET") |
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""" | |
This module imports necessary libraries from PyTorch, torchvision, and sets up the device configuration for CUDA if available. | |
It then sets hyperparameters for an application of a fully connected neural network on the MNIST dataset of handwritten digits. | |
The MNIST dataset is loaded, transformed into tensors, and split into a training and a test dataset. | |
The fully connected neural network architecture is defined with one hidden layer, after which the model is initiated. | |
CrossEntropyLoss is used as the loss function, and Adam optimizer is used for optimization. | |
Training is done for the specified number of epochs, where for each epoch, forward pass is performed, loss is calculated, | |
backpropagation is applied, and the model parameters are updated. |
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<opml version="1.0"> | |
<head> | |
<dateCreated>Mon, 21 Nov 2022 20:41:45 +0000</dateCreated> | |
</head> | |
<body> | |
<outline text="FastML" title="FastML" type="rss" xmlUrl="http://fastml.com/atom.xml" htmlUrl="http://fastml.com/"/> | |
<outline text="colah's blog" title="colah's blog" type="rss" xmlUrl="http://colah.github.io/rss" htmlUrl="http://colah.github.io/"/> | |
<outline text="Math ∩ Programming" title="Math ∩ Programming" type="rss" xmlUrl="https://jeremykun.com/feed/" htmlUrl="https://jeremykun.com"/> | |
<outline text="Pete Warden's blog" title="Pete Warden's blog" type="rss" xmlUrl="https://petewarden.com/feed/" htmlUrl="https://petewarden.com"/> | |
<outline text="Two Minute Papers" title="Two Minute Papers" type="rss" xmlUrl="https://www.youtube.com/feeds/videos.xml?channel_id=UCbfYPyITQ-7l4upoX8nvctg" htmlUrl="https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg"/> |
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<?xml version="1.0" encoding="UTF-8"?> | |
<opml version="1.0"> | |
<head> | |
<title>RSS subscriptions for [email protected]</title> | |
<dateCreated>Sat, 27 Feb 2021 17:33:45 +0000</dateCreated> | |
<ownerEmail>[email protected]</ownerEmail> | |
</head> | |
<body> | |
<outline text="Google AI Blog" title="Google AI Blog" type="rss" xmlUrl="http://feeds.feedburner.com/blogspot/gJZg" htmlUrl="http://ai.googleblog.com/"/> | |
<outline text="FastML" title="FastML" type="rss" xmlUrl="http://fastml.com/atom.xml" htmlUrl="http://fastml.com/"/> |
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# -*- coding: utf-8 -*- | |
""" Deletes all tweets below a certain retweet threshold. | |
""" | |
import tweepy | |
from datetime import datetime | |
# Constants | |
CONSUMER_KEY = '' |
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### Keybase proof | |
I hereby claim: | |
* I am chrisalbon on github. | |
* I am chrisalbon (https://keybase.io/chrisalbon) on keybase. | |
* I have a public key ASBI8zFqR11PMiDce5FTxa7M57P8WLTxMkdLD-tt72qjjAo | |
To claim this, I am signing this object: |
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import pandas as pd | |
index = pd.date_range('1/1/2000', periods=20000, freq='30min') | |
series = pd.Series(range(20000), index=index) | |
series.groupby(series.index.hour).sum() |
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{'Alabama': 4849377, | |
'Alaska': 736732, | |
'Arizona': 6731484, | |
'Arkansas': 2966369, | |
'California': 38802500, | |
'Colorado': 5355866, | |
'Connecticut': 3596677, | |
'Delaware': 935614, | |
'District of Columbia': 658893, | |
'Florida': 19893297, |
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Testing Popily Embeds In Project Jupyter" | |
] | |
}, | |
{ |
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