うつ病になってからの取り組み

研究生活の中で倒れ、起き上がれなくなった。

手足が鉛のように重く、身動きができなくなった。

また、味のする食べ物が受け付けなくなり、乾パンと水しか喉を通らなくなった。

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女声の出し方や練習方について 男性向け メモ

エッジボイスに近いやり方?

歌のためのボイトレのやり方を知っていると感覚を掴みやすいかもしれない。

手順

  1. 舌を出しながら声を出す
  2. 喉の力の入り具合を維持しながら舌を戻す
  3. 力の入るポイントをより背中側や上側にズラす
  4. 音程を上げる

3と4の手順を試行錯誤して自分の理想の声を探る

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On the use of distributed representations bagging for class classification and generalization performance

After the distributed representation has been obtained, the After the distributed representation is obtained, machine learning can be used to classify it.

Models that can be used include

  • Decision Tree
  • SVM Support Vector Machine
  • NN Neural Networks

and others.

SVM is included in NN in a broad sense.

In this section, we will use the decision tree method.

Bagging

  • Image of majority voting with multiple decision trees
  • Simple theory
    • Decision trees are highly explainable and are a classic machine learning model.
    • Computational load is light compared to deep learning
      • Depends on the size of the model
  • Not much explainability
    • Do we want to analyze each of the multiple decision trees?

``py from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier

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How to train a Japanese model with Sentence transformer to get a distributed representation of a sentence

. BERT is a model that can be powerfully applied to natural language processing tasks.

However, it does not do a good job of capturing sentence-wise features.

Some claim that sentence features appear in [ CLS\ ], but This paper](https://arxiv.org/abs/1908.10084) claims that it does not contain that much useful information for the task.

Sentence BERT is a model that extends BERT to be able to obtain features per sentence.

The following are the steps to create Sentence BERT in Japanese.

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deeplubcut 動画を対象にした点の位置の予測

点のアノテーションの予測

  • 蝿の腹、マウスの脊椎、指の関節など応用範囲が広い。
  • 動画でできてる。デモがある。
  • 動画の特徴量抽出はResNet, mobileNetなど
    • Mobile Netでできるならエッジコンピューティングが視野に入る
    • ラズパイ+GPUみたいな構成

参考リンク

https://github.com/DeepLabCut/DeepLabCut

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