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Olivier RISSER-MAROIX VieVie31

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View mmr.py
from sklearn.metrics.pairwise import cosine_similarity
def maximal_marginal_relevance(sentence_vector, phrases, embedding_matrix, lambda_constant=0.5, threshold_terms=10):
"""
Return ranked phrases using MMR. Cosine similarity is used as similarity measure.
:param sentence_vector: Query vector
:param phrases: list of candidate phrases
:param embedding_matrix: matrix having index as phrases and values as vector
:param lambda_constant: 0.5 to balance diversity and accuracy. if lambda_constant is high, then higher accuracy. If lambda_constant is low then high diversity.
:param threshold_terms: number of terms to include in result set
@kernel1994
kernel1994 / top_k.py
Last active Feb 11, 2021
Top k element index in numpy array
View top_k.py
import numpy as np
def largest_indices(array: np.ndarray, n: int) -> tuple:
"""Returns the n largest indices from a numpy array.
Arguments:
array {np.ndarray} -- data array
n {int} -- number of elements to select
@kylemcdonald
kylemcdonald / ACAI (PyTorch).ipynb
Last active Feb 20, 2021
PyTorch ACAI (1807.07543).
View ACAI (PyTorch).ipynb
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@weiliu620
weiliu620 / Dice_coeff_loss.py
Last active Dec 12, 2020
Dice coefficient loss function in PyTorch
View Dice_coeff_loss.py
def dice_loss(pred, target):
"""This definition generalize to real valued pred and target vector.
This should be differentiable.
pred: tensor with first dimension as batch
target: tensor with first dimension as batch
"""
smooth = 1.
@ruanbekker
ruanbekker / cheatsheet-elasticsearch.md
Last active Mar 3, 2021
Elasticsearch Cheatsheet : Example API usage of using Elasticsearch with curl
View cheatsheet-elasticsearch.md
View how-to-make-a-racist-ai-without-really-trying.ipynb
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@flyyufelix
flyyufelix / readme.md
Last active Feb 1, 2021
Resnet-101 pre-trained model in Keras
View readme.md

ResNet-101 in Keras

This is an Keras implementation of ResNet-101 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
@dusty-nv
dusty-nv / pytorch_jetson_install.sh
Last active Mar 2, 2021
Install procedure for pyTorch on NVIDIA Jetson TX1/TX2 with JetPack <= 3.2.1. For JetPack 4.2 and Xavier/Nano/TX2, see https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/
View pytorch_jetson_install.sh
#!/bin/bash
#
# EDIT: this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip
# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)
@ericshape
ericshape / flight_prediction.py
Created Dec 19, 2016
Flight Prediction Python Code
View flight_prediction.py
import numpy as np
import scipy as sp
import pandas as pd
import sklearn
from matplotlib import pyplot as plt
from sklearn import preprocessing
from sklearn.cross_validation import cross_val_predict
from sklearn import metrics
from sklearn.metrics import classification_report
from itertools import cycle
@angeris
angeris / domino_counter_example.txt
Last active Jul 13, 2018
Greedy Counterexample for Dominos
View domino_counter_example.txt
Initial positions
Player A:
[0-5] [1-3] [1-4] [2-2] [3-4] [4-4] [5-6]
Player B:
[0-0] [0-1] [0-6] [1-1] [1-6] [2-3] [2-6]
Player C:
[0-2] [2-4] [2-5] [3-3] [3-6] [4-5] [4-6]
Player D:
[0-3] [0-4] [1-2] [1-5] [3-5] [5-5] [6-6]