List generated by manually parsing the source code of the NCERT Textbook download page
This is a generic doc for any directional discussion. You can mold it to your use case (e.g. major eng initiative, team strategy, partnership alignment, etc)
Where are we today? Explain relevant context for the reader to understand the problem or opportunity you’re going after.
You might not have all the answers for how we can do better next time. You can use this doc to structure retrospective discussions with your team.
Overview:
- What broke?
- Why did it break? What was the root cause?
- Who does the breakage affect? How severe was it?
Design docs are a way to propose future work and get detailed technical feedback.
Brief description of what the problem or opportunity is. Give an overview of the domain and pain points. What is the current solution? Give some details about what its shortcomings are.
# Assumes you've awk, jq, curl installed | |
calc() { awk "BEGIN{print $*}"; } | |
export PROPERTY_VALUE=1000000 | |
export LOAN_AMOUNT=`calc $PROPERTY_VALUE*0.8` | |
export PROPERTY_ZIP=94107 | |
alias bankrate="curl 'https://mortgage-api.bankrate.com/rates/v4/?loanType=purchase&propertyValue=$PROPERTY_VALUE&propertyType=SingleFamily&propertyUse=PrimaryResidence&cashOutAmount=0&zipCode=$PROPERTY_ZIP&loanAmount=$LOAN_AMOUNT&creditScore=770&debtToIncomeRatio=0&pointsRange=Zero&productFamilies\[\]=conventional&loanTerms\[\]=30yr&loanTerms\[\]=7-1arm&loanTerms\[\]=7-6arm&loanTerms\[\]=10-1arm&loanTerms\[\]=10-6arm&defaultSearch=true&pid=br3&veteranStatus=NoMilitaryService&hadPriorVaLoan=false&hasVaDisabilities=false&firstTimeHomeBuyer=false&displayTargets\[\]=mobileRateTable&displayTargets\[\]=featuredRateTable&deviceTypes\[\]=mobile&e2eTestEnabled=false&clientId=MortgageRateTable&includeSponsored=true&includeEditorial=true' -H 'authority: mortgage-api.bankrate.com' -H 'accept: application/json, text/plain, */*' -H 'accept |
!pip install cython==0.28.5 | |
!pip install mmdet==2.10.0 requests | |
!pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html | |
!pip install mmcv-full==1.2.7+torch1.7.0+cu110 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html | |
!pip install onnx onnxruntime onnxruntime-gpu onnxoptimizer | |
!git clone --branch v2.10.0 https://github.com/open-mmlab/mmdetection.git | |
# Download conf and weights from | |
# https://github.com/iiLaurens/CascadeTabNet/blob/mmdet2x/Demo/Cascade_Tabnet_mmdet_v2_cpu_only_demo.ipynb |
# Execute this in a Jupyter notebook | |
import os | |
import json | |
import base64 | |
import pandas as pd | |
from pprint import pprint | |
# Import USD 10B+, 1MM vol+, 25+ P/E, Buy/Strong Buy rated | |
buy_rated_tradingview = pd.read_csv('~/Downloads/america_2021-04-11.csv') |
I hereby claim:
- I am pbamotra on github.
- I am benzene (https://keybase.io/benzene) on keybase.
- I have a public key ASCnWFSySbSBalyy4SCcjIzFTkb2gGmffCtnRewv0sLT2wo
To claim this, I am signing this object:
import datetime | |
import glob | |
from lxml import etree | |
import pandas as pd | |
def get_books(file): | |
doc = etree.HTMLParser() | |
tree = etree.parse(file, parser=doc) |