The Summer Training Summary-- the first

The Summer Training Summary-- the first A - vector的使用 UVa 101 关于vector 的几个注意点 vector p p.push_back(); 送入数据 p.insert() 指定位置指定元素,指定位置n个相同元素,指定位置区间元素 p.assign(); 赋值 p.pop_back(); 移除最后一个数据 p.erase(); 删除指定位置或者指定区间数据 p.resize();改变容器大小相当于保留N个元素 p.clear()…

TensorFlow入门学习(让机器/算法帮助我们作出选择)

catalogue . 个人理解 . 基本使用 . MNIST(multiclass classification)入门 . 深入MNIST . 卷积神经网络:CIFAR- 数据集分类 . 单词的向量表示(Vector Representations of Words) . 循环神经网络(RNN).LSTM(Long-Short Term Memory, LSTM) . 用深度学习网络搭建一个聊天机器人 0. 个人理解在学习的最开始,我在这里写一个个人对deep leanring和神经网络的粗…

Transformer解析与tensorflow代码解读

本文是针对谷歌Transformer模型的解读,根据我自己的理解顺序记录的. 另外,针对Kyubyong实现的tensorflow代码进行解读,代码地址https://github.com/Kyubyong/transformer 这里不会详细描述Transformer的实现机理,如果有不了解Transformer的可以先阅读文章<Attention is all you need>,以及我列出的一些参考博客,都是不错的解读. Layer Normalization 首先是Layer Norm…

XGBoost缺失值引发的问题及其深度分析

1. 背景 XGBoost模型作为机器学习中的一大“杀器”,被广泛应用于数据科学竞赛和工业领域,XGBoost官方也提供了可运行于各种平台和环境的对应代码,如适用于Spark分布式训练的XGBoost on Spark.然而,在XGBoost on Spark的官方实现中,却存在一个因XGBoost缺失值和Spark稀疏表示机制而带来的不稳定问题. 事情起源于美团内部某机器学习平台使用方同学的反馈,在该平台上训练出的XGBoost模型,使用同一个模型.同一份测试数据,在本地调用(Java引擎)与…

(转) Summary of NIPS 2016

转自:http://blog.evjang.com/2017/01/nips2016.html Eric Jang Technology, A.I., Careers Monday, January 2, 2017 Summary of NIPS 2016 The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona…

[CS231n-CNN] Training Neural Networks Part 1 : activation functions, weight initialization, gradient flow, batch normalization | babysitting the learning process, hyperparameter optimization

课程主页:http://cs231n.stanford.edu/ Introduction to neural networks -Training Neural Network ______________________________________________________________________________________________________________________________________________________________…

用matlab训练数字分类的深度神经网络Training a Deep Neural Network for Digit Classification

This example shows how to use Neural Network Toolbox™ to train a deep neural network to classify images of digits. Neural networks with multiple hidden layers can be useful for solving classification problems with complex data, such as images. Each l…

【Android Developers Training】 81. 解析XML数据

注:本文翻译自Google官方的Android Developers Training文档,译者技术一般,由于喜爱安卓而产生了翻译的念头,纯属个人兴趣爱好. 原文链接:http://developer.android.com/training/basics/network-ops/xml.html 可扩展标记语言(XML)是一种将文档编码为机器可阅读的形式的规则集合.XML是一种在互联网中分享数据的比较流行的格式.那些频繁更新内容的网站(如新的站点或者博客),经常会提供一个XML源,这样外部程序…

Summary on deep learning framework --- PyTorch

Summary on deep learning framework --- PyTorch Updated on 2018-07-22 21:25:42 import osos.environ["CUDA_VISIBLE_DEVICES"]="4" 1. install the pytorch version 0.1.11 ## Version 0.1.11 ## python2.7 and cuda 8.0 sudo pip install http://…

Summary on deep learning framework --- Theano && Lasagne

Summary on deep learning framework --- Theano && Lasagne 2017-03-23 1. theano.function output = input ** 2 f = theano.function([input], output) print(f(3)) >> the output is: 3^2 = 9. 2. verbose = 1 or 0, does it have any difference ? so…

Summary on deep learning framework --- Torch7

Summary on deep learning framework --- Torch7 2018-07-22 21:30:28 1. 尝试第一个 CNN 的 torch版本, 代码如下: -- We now have 5 steps left to do in training our first torch neural network -- 1. Load and normalize data -- 2. Define Neural Network -- 3. Define Loss…

tensorflow简单记录summary方法

虽然tf官方希望用户把 train , val 程序分开写,但实际开发中,明显写在一起比较简单舒服,但在保存数据到 summary 时, val 部分和 train 部分不太一样,会有一些问题,下面讨论如何在这种情况下记录 train/val 的 summary . 假设训练时的主要代码结构如下: losssummary = ...othersummary = ...trainsummaries = tf.summary.merge([losssummary, othersummary]) fo…

A Summary of Multi-task Learning

A Summary of Multi-task Learning author by Yubo Feng. Intro In this paper[0], the introduction of multi-task learning through the data hungry, the most common problem of Deep Learning[1]. Basic assumption: tasks are related. MTL mimic human learning…

[Tensorflow] Object Detection API - build your training environment

一.前期准备 Prepare protoc Download Protocol Buffers Create folder: protoc and unzip it. unsw@unsw-UX303UB$ ls models Others protoc train_data unsw@unsw-UX303UB$ ls protoc/ bin include readme.txt unsw@unsw-UX303UB$ ls protoc/bin/ protoc Prepare model Down…

Summary on Visual Tracking: Paper List, Benchmarks and Top Groups

Summary on Visual Tracking: Paper List, Benchmarks and Top Groups 2018-07-26 10:32:15 This blog is copied from: https://github.com/foolwood/benchmark_results Thanks for the careful list of visual tracking provided by foolwood Visual Trackers CVPR20…

通俗易懂之Tensorflow summary类 & 初识tensorboard

前面学习的cifar10项目虽小,但却五脏俱全.全面理解该项目非常有利于进一步的学习和提高,也是走向更大型项目的必由之路.因此,summary依然要从cifar10项目说起,通俗易懂的理解并运用summary是本篇博客的关键. 先不管三七二十一,列出cifar10中定义模型和训练模型中的summary的代码: # Display the training images in the visualizer. tf.summary.image('images', images) def _activ…

Bit error testing and training in double data rate (ddr) memory system

DDR PHY interface bit error testing and training is provided for Double Data Rate memory systems. An integrated circuit comprises a bit error test (BERT) controller that provides a bit pattern; and a physical interface having a plurality of byte lane…

Method for training dynamic random access memory (DRAM) controller timing delays

Timing delays in a double data rate (DDR) dynamic random access memory (DRAM) controller (114, 116) are trained. A left edge of passing receive enable delay values is determined (530). A final value of a receive data strobe delay value and a final va…

湖南省第十三届大学生计算机程序设计竞赛 Football Training Camp 贪心

2007: Football Training Camp[原创-转载请说明] Submit Page Summary Time Limit: 1 Sec Memory Limit: 128 Mb Submitted: 228 Solved: 30 Description 在一次足球联合训练中一共有n支队伍相互进行了若干场比赛. 对于每场比赛,赢了的队伍得3分,输了的队伍不得分,如果为平局则两支队伍各得1分. Input 输入包含不超过1000组数据. 每组数据的第…

Summary of Critical and Exploitable iOS Vulnerabilities in 2016

Summary of Critical and Exploitable iOS Vulnerabilities in 2016 Author:Min (Spark) Zheng, Cererdlong, Eakerqiu @ Team OverSky 0x00 Introduction iOS security is far more fragile than you believe. And there are lots of critical and exploitable iOS vuln…