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naturale0 / send_pdf2rM.py
Created December 4, 2021 14:06
Convert PDF to reMarkable notebook, then send it to rM device
View send_pdf2rM.py
#!/usr/bin/env python
# coding: utf-8
# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"></ul></div>
# In[36]:
import os
@naturale0
naturale0 / backup_reMarkable.py
Last active December 21, 2021 03:30
Backup reMarkable documents as PDFs
View backup_reMarkable.py
#!/usr/bin/env python
# coding: utf-8
## =============== REQUIREMENTS =============== ##
## rmrl: https://github.com/naturale0/rmrl
## rsync (for rM):
## - https://github.com/JBBgameich/rsync-static/releases/download/continuous/rsync-arm
## - https://www.reddit.com/r/RemarkableTablet/comments/hetyfa/comment/fvu1fy3/?utm_source=share&utm_medium=web2x&context=3
# In[36]:
@naturale0
naturale0 / install_ds_packages.sh
Last active June 1, 2021 10:59
install basic ds packages in termux
View install_ds_packages.sh
pkg update
pkg install python build-essential freetype libpng libzmq libcrypt libjpeg-turbo libxml2 libxslt
pip install bs4 requests lxml
curl -LO https://its-pointless.github.io/setup-pointless-repo.sh
bash setup-pointless-repo.sh
pkg install numpy scipy
pip install matplotlib jupyter
View nonparametric function estimation.ipynb
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View translation_char_example.py
import itertools
from torchtext.experimental.datasets import TextClassificationDataset
from torchtext.vocab import build_vocab_from_iterator
from torchtext.experimental.functional import sequential_transforms
from torchtext.experimental.datasets import IMDB
from torchtext.data.utils import get_tokenizer
from torch.nn.utils.rnn import pack_padded_sequence
def build_char_vocab(data, index, bow="<w>", eow="</w>"):
@naturale0
naturale0 / NLPM_pytorch.ipynb
Last active February 5, 2021 08:45
implementation of NPLM (pytorch & tensorflow)
View NLPM_pytorch.ipynb
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View quantile_normalize_example.py
# In []:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sp
A = np.random.normal(5, 1, 100)
B = np.random.normal(3, 1.5, 100)
C = np.random.normal(4, 2, 100)
@naturale0
naturale0 / pretrain-WSL.ipynb
Created June 17, 2018 14:41
Chest X-ray (Pneumonia) - WSL
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@naturale0
naturale0 / Weakly-supervised_localizer.ipynb
Last active June 17, 2018 14:02
Facial Emotion Recognition - WSL
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@naturale0
naturale0 / Understanding_ANN_3.ipynb
Created April 18, 2018 14:37
training as testing a 2-layer NN
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