ducnh1022

from flask import Flask, url_for
from flask import request
from flask import json
from flask import Response
from flask import jsonify

from gevent.pywsgi import WSGIServer

import mysql.connector
from mysql.connector import Error

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ducnh1022

Proof of stake - Casper
https://tendermint.com/static/docs/tendermint.pdf
https://arxiv.org/pdf/1406.5694.pdf
https://github.com/ethereum/research/blob/master/papers/CasperTFG/CasperTFG.pdf
https://github.com/ethereum/research/blob/master/papers/casper-basics/casper_basics.pdf
https://github.com/ethereum/research/blob/master/papers/casper-economics/casper_economics_basic.pdf
https://github.com/ethereum/research/blob/master/papers/cbc-consensus/AbstractCBC.pdf
https://github.com/ethereum/research/blob/master/papers/censorship_rejection/censorship_rejection.pdf
https://github.com/ethereum/research/blob/master/papers/other_casper/discouragement.pdf

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Verifying my Blockstack ID is secured with the address 1E4LMRyD9DFvMcUh3cBMStEnqxSxELdpgU https://explorer.blockstack.org/address/1E4LMRyD9DFvMcUh3cBMStEnqxSxELdpgU

ducnh1022

# ETHLend.io White Paper - Democratizing Lending

19 November 2017

<p>Abstract: ETHLend.io introduces decentralized lending on Ethereum network by using ERC-20 compatible tokens or Ethereum Name Service (ENS) domains as a collateral. ETHLend solves the problem on reducing the loss of loan capital on default. On healthy loan relationships the loan is paid back. However, the pseudo-anonymous nature of Ethereum blockchain network opens the possibility to avoid repayment of the loan since the lender might not have all the necessary details of the borrower to enforce the debt in the borrower's jurisdiction. Moreover, enforcement in a decentralized environment, where the parties can be from any part of the world, might not be efficient. ETHLend provides decentralized solutions to avoid loss of capital and to make one true global lending market available.</p>

<p>Copyright 2017 ETHLend.io</p>

<p>Without explicit permission, anyone has the right to use, reproduce or distribute any material in this white paper for n

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msgstr "Tiếng Anh"
msgstr "Tiếng Tây Ban Nha"
msgstr "Tiếng Trung Quốc"
msgstr "Tiếng Đức"
msgstr "Tiếng Hàn"
msgstr ""
msgstr ""
msgstr ""
msgstr ""

ducnh1022

gradient descent wont scale

Stochastic gradient use 1 data point, on average, it increases likelihood, sometimes decrease, "noisy" convergence

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Precision: fraction of positive predictions that are actually positive
recall: fraction of positive data predicted to be positive

optimistic = low precision high recall
pesstimistic = high precision low recall

trade off

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Logític clasìication -> stochastic ooptimization 
-> data and parameter tuning -> deep networks  
-> regularization -> convolutional networks 
-> embeddings -> recurrent models

deep learning apply in all field, reasearcher, engineer, data scientist

ducnh1022

combine multiple simple classifier -> ensemble classifiers

y hat = sign(f(x))

Adaboost

start same weight for all points alpha = 1/N

For t = 1..T
learn f(t) with data weight alpha