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@bwhite
bwhite / rank_metrics.py
Created September 15, 2012 03:23
Ranking Metrics
"""Information Retrieval metrics
Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np
@sloria
sloria / bobp-python.md
Last active June 18, 2024 08:18
A "Best of the Best Practices" (BOBP) guide to developing in Python.

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

  • "Build tools for others that you want to be built for you." - Kenneth Reitz
  • "Simplicity is alway better than functionality." - Pieter Hintjens
@bsweger
bsweger / useful_pandas_snippets.md
Last active April 19, 2024 18:04
Useful Pandas Snippets

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

@nderkach
nderkach / read_mitmproxy_dumpfile.py
Last active April 18, 2023 21:36
Read a mitmproxy dump file and generate a curl command
#!/usr/bin/env python
#
# Simple script showing how to read a mitmproxy dump file
#
### UPD: this feature is now avaiable in mitmproxy: https://github.com/mitmproxy/mitmproxy/pull/619
from libmproxy import flow
import json, sys
@karpathy
karpathy / pg-pong.py
Created May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
@fchollet
fchollet / classifier_from_little_data_script_1.py
Last active May 15, 2024 07:19
Updated to the Keras 2.0 API.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
@leonardofed
leonardofed / README.md
Last active June 17, 2024 14:54
A curated list of AWS resources to prepare for the AWS Certifications


A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.


#determine the labels
import pyvw #vw python interface
DEST = 1
PROP = 2
FAC = 3
...
#create the class for the Sequence Labeler
class SequenceLabeler(pyvw.SearchTask):
import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
# Loading datasets'
@jinyu121
jinyu121 / get_anchor.py
Last active March 5, 2024 02:36
YOLO2 Get Anchors
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import os
import random