lzghzr

需要的工具

固件工具 NVIDIA Firmware Tools (MFT)
固件工具 4.3.0.25版本 NVIDIA Firmware Tools (MFT) 4.3.0.25
HPE固件 fw-ConnectX3Pro-rel-2_42_5700-764285-B21_Ax-CLP-8025-UEFI-14.11.49-FlexBoot-3.4.754.tgz
自定义固件 ConnectX3Pro-rel-2_40_5030.tgz
固件ini修改 mft-scripts

准备工作

jrzaurin

focal_loss = lambda x,y: focal_loss_lgb(x, y, alpha=0.25, gamma=1.)
focal_loss_eval = lambda x,y: focal_loss_lgb_eval_error(x, y, alpha=0.25, gamma=1.)
model = lgb.train(best, self.lgtrain, fobj=focal_loss, feval=focal_loss_eval)

ii-research-yu

def lightgbm_custom_obj_lambdarank(preds, train_data, first_order=False, constant_hessian=1.0):
    '''
    :param preds:      numpy.ndarray of shape (size_data, )
    :param train_data:
    :return:
    '''
    all_labels = train_data.get_label()  # numpy.ndarray of shape (size_data, )
    group = train_data.get_group()       # numpy.ndarray of shape (num_queries, )

    size_data = len(all_labels)

mbinna

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

allwefantasy

import tensorflow as tf
import random
import numpy as np
import time

BASIC_HOME = "/Users/allwefantasy/Downloads"
WORD_VECTOR_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_word_embedding'
WORD_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_keywords2'
WORD_RESULT_VECTOR_FILE = BASIC_HOME + '/laiwen/WORD_RESULT_VECTOR_FILE4'
MODEL_SAVE_DIR = BASIC_HOME + '/laiwen/model/autoencoder'

mommi84

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding

huming2207

OpenWrt 15.05 Chaos Calmer Linux Kernel Configuration for Seavo SV3-260xx

What it is

This is a Linux Kernel 3.18 configuration for Seavo SV3-260xx series motherboard.

It has:

• Intel Atom N2600 dual-core CPU
• Intel GMA3650 (SGX545) GPU
• Several RS232 serial ports

gitaarik

Git Submodules basic explanation

Why submodules?

In Git you can add a submodule to a repository. This is basically a repository embedded in your main repository. This can be very useful. A couple of usecases of submodules:

• Separate big codebases into multiple repositories.

bwhite

"""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

halpo

Building a beamer presentation with knitr.

Introduction

The documents included are the input for knitr. In addition you need to have the tool pandoc installed. I also use a custom beamer template to add the University of Utah \institute command to the template. It also changes the indentation some.

Steps

1. knit document with