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@jmoz
jmoz / rsi.py
Created Sep 27, 2019
RSI calculation to match Tradingview
View rsi.py
import pandas as pd
def rsi(ohlc: pd.DataFrame, period: int = 14) -> pd.Series:
"""See source https://github.com/peerchemist/finta
and fix https://www.tradingview.com/wiki/Talk:Relative_Strength_Index_(RSI)
Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements.
RSI oscillates between zero and 100. Traditionally, and according to Wilder, RSI is considered overbought when above 70 and oversold when below 30.
Signals can also be generated by looking for divergences, failure swings and centerline crossovers.
@kylemcdonald
kylemcdonald / Triplet Loss.ipynb
Last active Jun 15, 2020
Experimenting with triplet loss embeddings.
View Triplet Loss.ipynb
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@zmjjmz
zmjjmz / ml_utils.py
Created Dec 19, 2017
shitty lookup layer
View ml_utils.py
class TokenizeLookupLayer(keras.layers.Layer):
"""
Layer that encapsulates the following:
- Tokenizing sentences by space (or given delimiter)
- Looking up the words with a given vocabulary list / table
- Resetting the shape of the above to be batch_size x pad_len (using dark magic)
# Input Shape
2D string tensor with shape `(batch_size, 1)`
# Output Shape
2D int32 tensor with shape `(batch_size, pad_len)`
@widdowquinn
widdowquinn / jupyterhub_aws.md
Created Dec 9, 2017
Set up JupyterHub on AWS
View jupyterhub_aws.md

JupyterHub on AWS

EC2 Setup

  • Log in to AWS
  • Go to a sensible region
  • Start a new instance with Ubuntu Trusty (14.04) - compute-optimised instances have a high vCPU:memory ratio, and the lowest-cost CPU time. c4.2xlarge is a decent choice.
  • Set security group (firewall) to have ports 22, 80, and 443 open (SSH, HTTP, HTTPS)
  • If you want a static IP address (for long-running instances) then select Elastic IP for this VM
  • If you want to use HTTPS, you'll probably need a paid certificate, or to use Amazon's Route 53 to get a non-Amazon domain (to avoid region blocking).
@yossorion
yossorion / what-i-wish-id-known-about-equity-before-joining-a-unicorn.md
Last active Aug 11, 2020
What I Wish I'd Known About Equity Before Joining A Unicorn
View what-i-wish-id-known-about-equity-before-joining-a-unicorn.md

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

@shagunsodhani
shagunsodhani / Batch Normalization.md
Last active Aug 4, 2020
Notes for "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper
View Batch Normalization.md

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

@walterreade
walterreade / xgb_hyperopt.py
Created Jan 16, 2016
XGBoost Hyperopt Gridsearch
View xgb_hyperopt.py
# http://www.dataiku.com/blog/2015/08/24/xgboost_and_dss.html
import dataiku
import pandas as pd, numpy as np
from dataiku import pandasutils as pdu
from sklearn.metrics import roc_auc_score
import xgboost as xgb
from hyperopt import hp, fmin, tpe, STATUS_OK, Trials
train = dataiku.Dataset("train").get_dataframe()
View raspberry-pi-vpn-router.md

Raspberry Pi VPN Router

This is a quick-and-dirty guide to setting up a Raspberry Pi as a "router on a stick" to PrivateInternetAccess VPN.

Requirements

Install Raspbian Jessie (2016-05-27-raspbian-jessie.img) to your Pi's sdcard.

Use the Raspberry Pi Configuration tool or sudo raspi-config to:

@elad
elad / neural-style-ec2.txt
Created Sep 7, 2015
Running neural-style in EC2
View neural-style-ec2.txt
Start a g2.2xlarge or better (GPU instance) with https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-ffba7b94
Login, username is ubuntu
Update a bunch of stuff and make sure cudnn R2 is used:
luarocks install image
luarocks install loadcaffe
luarocks install torch
export LD_LIBRARY_PATH=/home/ubuntu/torch-distro/install/lib:/home/ubuntu/torch-distro/install/lib:/home/ubuntu/cudnn-6.5-linux-x64-v2-rc2
@jpetazzo
jpetazzo / README.md
Last active Aug 8, 2020
Manual custom geocoding using OSM database
View README.md

Someone asked how to get the latlong from a specific road near a town on OpenStreetMap.

If you need to do it only once (e.g., you're about to go on a trip, and your GPS cannot find your destination city, but allows you to enter GPS coordinates), you can use Nominatim, OpenStreetMap's geocoding interface.

If you need to do it multiple times, in a programmatic manner, there are at least two ways to do that.

Note: I worked with OSM data a couple of years ago, but I don't have an OSM database on my local laptop right now, so some instructions will be a bit fuzzy. I do apologize in advance.

PostGIS queries on a local OSM DB

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