Rodrigo Formigone formigone

## notebook.json
{
  "toc2": {
    "number_sections": false,
    "skip_h1_title": false,
    "toc_cell": false
  },
  "load_extensions": {
    "plotlywidget/extension": true,
    "jupyter-js-widgets/extension": true,
    "contrib_nbextensions_help_item/main": true,

## arxiv-abstracts-to-markdown.py
'''
The purpose of this script is to quickly collect a large number of abstracts
from argiv.org so to simplify scanning lots of documents on a single topic
before committing the time to do an in-depth reading of any particular paper.

@author formigone
'''

import requests
import re

## dataclass_defaults_p36.py
from collections import namedtuple

_Activity = namedtuple('Activity', ['activityId', 'studentId', 'timeRead'])
_Activity.__new__.__defaults__ = (None, None, 0.0)


class Activity(_Activity):
    @staticmethod
    def from_dict(args):
        args = {k: v for k, v in args.items() if k in _Activity._fields}

## custom.css
#notebook-container{
    box-shadow: none !important;
}

.container {
    width: 80% !important;
}

.notebook_app {
    background: #fff !important;

## appsync_fetch.py
query = '''
{
  users {
    id
    created
    firstName
    lastName
    email
  }
}

## model.py
def model_fn(features, labels, mode, params):
    word_vec = embed_sequence(features, vocab_size=params['vocab_size'], embed_dim=params['embed_size'])
    # [-1, seq_len, embed_size]

    word_list = tf.unstack(word_vec, axis=1)
    # [-1, embed_size]

    cell = tf.nn.rnn_cell.GRUCell(params['embed_size'])
    _, encoding = tf.nn.static_rnn(cell, word_list, dtype=tf.float32)


## tfrecord_utils.py
def list_to_tfrecord(list, tfrecord_filename):
  """
  Convert a list of (features, labels) to a TFRecord file.

  param list: a list of tuples with (feature, label)
  """
   with python_io.TFRecordWriter(tfrecord_filename) as writer:
      for feature, label in list:
        example = tf.train.Example()
        example.features.feature['x'].float_list.value.extend(features)

## matrix_transpose.js
function transpose(mat){
  return mat[0].map((_, y) => {
    return mat.map((_, x) => {
      return mat[x][y];
    });
  });
}

var mat0 = [
  [0, 1, 2, 3, 4],

## matrix_multipication.js
function mult(a, b){
  return a.map((row) => {
    return b[0].map((col, j) => {
      return row.reduce((acc, a0, i) => (acc + a0 * b[i][j]), 0);
    });
  })
}

var mat0 = [
  [1, 2, 3, 4],

## react-custom-html5-video-player.html
<!doctype html>
<html>
<head>
   <title>mp4</title>

   <style>
       .emVid {
           position: relative;
           bottom: 0;
           left: 0;
	{
	"toc2": {
	"number_sections": false,
	"skip_h1_title": false,
	"toc_cell": false
	},
	"load_extensions": {
	"plotlywidget/extension": true,
	"jupyter-js-widgets/extension": true,
	"contrib_nbextensions_help_item/main": true,
	'''
	The purpose of this script is to quickly collect a large number of abstracts
	from argiv.org so to simplify scanning lots of documents on a single topic
	before committing the time to do an in-depth reading of any particular paper.

	@author formigone
	'''

	import requests
	import re
	from collections import namedtuple

	_Activity = namedtuple('Activity', ['activityId', 'studentId', 'timeRead'])
	_Activity.__new__.__defaults__ = (None, None, 0.0)


	class Activity(_Activity):
	@staticmethod
	def from_dict(args):
	args = {k: v for k, v in args.items() if k in _Activity._fields}
	#notebook-container{
	box-shadow: none !important;
	}

	.container {
	width: 80% !important;
	}

	.notebook_app {
	background: #fff !important;
	def model_fn(features, labels, mode, params):
	word_vec = embed_sequence(features, vocab_size=params['vocab_size'], embed_dim=params['embed_size'])
	# [-1, seq_len, embed_size]

	word_list = tf.unstack(word_vec, axis=1)
	# [-1, embed_size]

	cell = tf.nn.rnn_cell.GRUCell(params['embed_size'])
	_, encoding = tf.nn.static_rnn(cell, word_list, dtype=tf.float32)
	def list_to_tfrecord(list, tfrecord_filename):
	"""
	Convert a list of (features, labels) to a TFRecord file.

	param list: a list of tuples with (feature, label)
	"""
	with python_io.TFRecordWriter(tfrecord_filename) as writer:
	for feature, label in list:
	example = tf.train.Example()
	example.features.feature['x'].float_list.value.extend(features)
	function transpose(mat){
	return mat[0].map((_, y) => {
	return mat.map((_, x) => {
	return mat[x][y];
	});
	});
	}

	var mat0 = [
	[0, 1, 2, 3, 4],
	function mult(a, b){
	return a.map((row) => {
	return b[0].map((col, j) => {
	return row.reduce((acc, a0, i) => (acc + a0 * b[i][j]), 0);
	});
	})
	}

	var mat0 = [
	[1, 2, 3, 4],
	<!doctype html>
	<html>
	<head>
	<title>mp4</title>

	<style>
	.emVid {
	position: relative;
	bottom: 0;
	left: 0;