machinatoonist

# How to analyse 2.2 million words from 786 different speeches and interviews
# by the Prime Minister of Australia from Jan 2020- July 2021.  After hearing 
# one of the PM's speeches I began to wonder if transcripts of all his
# speeches and interviews were publicly available.  It turns out they are!

# This code extract shows how I scraped the full text of 786 speeches and interviews
# using the #rvest package which comes with tidyverse.

# I recommend @Julia Silge's #Tidytext tools for analysis.

machinatoonist

library(correlationfunnel)
library(dplyr)

data("ames")

data_prep <- ames

### Step 1 - Prepare Data as Binary Features

# We use the `binarize()` function to produce a feature set of binary (0/1) variables.

machinatoonist

def hello():
  return x+y
       

machinatoonist

This notes is written by Sheldon. You can find me with #iOSBySheldon in Github, Youtube, Facebook, etc.

Need

Convert .mov/.MP4 to .gif

Reason

As a developer, I feel better to upload a short video when I create the pull request to show other viewers what I did in this PR. I tried .mov format directly got after finishing recording screen using Quicktime, however, gif offers preview in most web pages, and has smaller file size.

This is not limited to developer, anyone has this need can use this method to convert the files.

machinatoonist

def train(arch, train_df=train_df, accum=1, epochs=12):
    dls = TextDataLoaders.from_df(train_df,text_col='desc',label_col='label',valid_pct=0.2,bs=64//accum)
    cbs = GradientAccumulation(64) if accum else []
    # learn = text_classifier_learner(dls, arch=AWD_LSTM, drop_mult=0.5, metrics=[accuracy], cbs=cbs).to_fp16()    
    learn = text_classifier_learner(dls, arch=AWD_LSTM(vocab_sz=200, emb_sz=20, n_hid=10, n_layers=2, pad_token=1, 
                                                       hidden_p=0.2, embed_p=0.02, input_p=0.1, weight_p=0.2), 
                                    drop_mult=0.5, metrics=[accuracy], cbs=cbs).to_fp16()    
    learn.fine_tune(epochs, 0.01)
    return learn