nickyongzhang
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| { | |
| "batch_size": 128, | |
| "kernel_size": 3, | |
| "seq_length": 20, | |
| "dropout": 0.2, | |
| "emb_dropout": 0.25, | |
| "clip": 0.4, | |
| "softmax_nsampled": 100, | |
| "embedding_size": 64, | |
| "lr": 0.001, |
nickyongzhang
/ rank_metrics.py
Created
April 16, 2019 17:51
— forked from bwhite/rank_metrics.py
Ranking Metrics
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| """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 |
nickyongzhang
/ PageRank-UUI.ipynb
Last active
March 18, 2019 09:24
This task aims at combining the PageRank model on UUI network with current FMT model to detect more fraud cases.
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nickyongzhang
/ UUI_network_analysis+shareVoucher+yongzhang.ipynb
Last active
February 17, 2019 14:45
Build network whose edges denoting users having the same pattern when using vouchers.
nickyongzhang
/ UUI_network_analysis+sharePayment+yongzhang.ipynb
Last active
February 13, 2019 08:41
Build network whose edges denoting users sharing bank accounts or credit cards
nickyongzhang
/ UUI_network_analysis+shareIP+yongzhang.ipynb
Last active
February 18, 2019 03:06
Build network whose edges denoting users appearing in the same IP address
nickyongzhang
/ UUI_network_analysis+shareDevice+yongzhang.ipynb
Last active
February 8, 2019 07:31
UUI network analysis
nickyongzhang
/ pg-pong.py
Created
March 20, 2017 12:49
— forked from karpathy/pg-pong.py
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
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| """ 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 |