## Overview

This article shows the simple example of`all_of`

from `algorithm`

library on C++.

`all_of`

from `algorithm`

library on C++.`transform`

function from algorithm library of C++ and its syntax.Although I’m not 100% sure, when we compare with before, I think the face detection has been reaching certain level of accuracy. If the picture is not so complex, with some accuracy, the faces are detected. With this face detection, relatively easily, we can anonymize human’s face.

This is my personal memo.

`sum`

function. This article is my personal memo about a book.
These days I've been on the journey of studying Functional programming and as one of the books, read Functional Kotlin.

I'll leave the review of it.

I'll leave the review of it.

I think there are many people who are on the journey of finding the best keyboard for themselves. I was on that. On this post, I’ll introduce my personal best buying of keyboard.

As a data scientist, I spend a lot of time in front of my PC. Although I'm not a work-environment geek, I more or less care about some points. One of those is**Keyboard**.

As a data scientist, I spend a lot of time in front of my PC. Although I'm not a work-environment geek, I more or less care about some points. One of those is

The following image is part of the data set. As you can see, this is composed of visually complex letters.

On this article, I’ll do simple introduction of Kuzushiji-MNIST and classification with Keras model.

DenseNets is composed of Dense blocks. It is expressed as the image below, which is quoted from https://arxiv.org/abs/1608.06993.

On the context of the history of convolutional neural network, ResNet helps the network to be deeper without degradation problem by the shortcut path to the output of the Residual module. DenseNets and Dense block is near concept from the different approach.

This article is to help to understand the basic concept of Dense block of DenseNets and how to write that. For coding, I’ll use Python and Keras.

About the ResNet and Residual module, please read the article below.

If you want to know the detail of DenseNets and Dense block, I recommend you read the article below.

When you find a mistake, please let me know by comment or mail.

The image above is from https://arxiv.org/abs/1512.03385.

Basically, deeper neural network contributes to the better outcome. If you have enough computational resource(unfortunately, I don't have), for difficult task, you can approach it with really deep neural network. However, with deeper neural network, the problem of degradation comes, which makes it difficult to train the model. Residual module offers one of the solutions to this problem, meaning that with this, we can make deeper neural network by softening the difficulty of training.

For precise and better understanding, I recommend that you read the paper below. Here, I'll just show summary for simple and concise understanding and coding with Keras.

If there are strange or wrong points, please let me know by comment or message.

On the article, AUC as an objective function: with Julia and Optim.jl package, I made a model with AUC objective function. The predicted score by that was distributed in really narrow area, because AUC objective function is based on the order without caring the distance from explained variable. With some evaluation norms, the model's score seems not nice.

About this point, just in case, I'll leave the simple experiment.

Julia: version 0.6.3

Julia: Version 0.6.3

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