Simple Convolutional Neural Network for Genomic Variant Calling with TensorFlow
AI/Machine Learning in Biotech StartupsNo doubt that the fast development of recent progress using deep neural network has change the way that we can solve various problem from image recognition to genomics. Many startups (Viome/CZ Biohub/Deep Genomics) has emphasize using “AI/machine learning” for their research and product. Other relative “big” biotech startups in Silicon Valley, e.g., Verily, C
Lua版 ゼロから作るDeep Learning その１３［ニューラルネットワークに関する勾配］ - Qiita
--Copyright (C) 2017 Kazuki Nakamae --Released under MIT License --license available in LICENSE file common = require './common' --- simpleNetクラス（オブジェクト） -- 単純なニューラルネットワークを生成する -- @param isConstW 重みを固定 {Type:Bool} simpleNet={} simpleNet.new = function(isConstW) local obj={} --メンバ変数 if isConstW then --原書で紹介されている重みを使用 obj.W = torch.Tensor({{0.47355232, 0.9977393, 0.84668094}, {0.85557411, 0.03563661
Difference b/w Jeremy's VGG16 and Keras built in VGG 16
I was exploring Keras a bit further, and found that VGG16 is also available to be loaded directly from the Keras api: https://keras.io/applications/#vgg16 Upon inspecting the pre-trained network provided in platform.ai/models vs. the one available in keras, the two networks appear to have different architectures. For example, VGG16 provided by Jeremy has dropout layers in between the dense layers,
Transfer Learning
Andrew Ng said during his widely popular NIPS 2016 tutorial that transfer learning, after supervised learning, will be the next driver of ML commercial success. What is Transfer Learning? Transfer learning is the transferring of knowledge gained from one model(trained on a significantly larger dataset) to another dataset with similar characteristics. For example, Imagenet contains images for 1000
Deep Learning Chapter 2: Linear Algebra
In the next few weeks we’ll be revisiting Applied Math and Machine Learning Basics. During our second meeting which took place on 5/8 we went over Chapter 2: Linear Algebra. (Book is available online for free: http://www.deeplearningbook.org/) Chapter was presented by Gavin Crooks. After we split up into small groups to solve some math problems. We also had a Q&A with special guest Yaroslav Bulato
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen
A Simple Neural Attentive Meta-Learner
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of th
A Walkthrough of Convolutional Neural Network — Hyperparameter Tuning
Among the diverse deep learning architecture, convolutional neural network (convnets for short) stands out for its unprecedented performance on computer vision. It is an artificial neural network inspired by the animal visual cortex and has been sucessfully applied to vision recognition tasks. In this article, I will provide a walkthrough on convnet, how to tune hyperparameters and how to visualiz
DNNに匹敵する新たな学習器であるDeep Forestの学習 - Qiita
Deep Learningの闇ではなく、Deep Forestの茂みの中に進みたいと思います。 DNNの代替となるかもしれないアルゴリズムにDeep Forestというものがあります。 Deep Forest :Deep Neural Networkの代替へ向けての記事を読んだり、論文を読んだりしてみると、どうやらランダムフォレストと呼ばれる決定木の集合体を複数個使い、幅および深さの方向に対してたくさん並べていくことで、Deepな構造にしているようです。 ランダムフォレストについてはこの記事が参考になります。pythonのモジュールであるscikit-learnと呼ばれる機械学習ツールにおけるランダムフォレストの説明となっていますが、今回走らせるコードもPythonで、しかもscikit-learnを使っているので、めちゃくちゃ参考になります。 Python以外にもR言語での実装があるよう
Image Augmentation for Deep Learning using Keras and Histogram Equalization
In this post we’ll go over: Image Augmentation: What is it? Why is it important?Keras: How to use it for basic Image Augmentation.Histogram Equalization: What is it? How is it useful?Implementing Histogram Equalization Techniques: one way to modify the keras.preprocessing image.py file.Image Augmentation: What is it? Why is it important?Deep Neural Networks, particularly Convolutional Neural Netwo
MicrosoftとPreferred Networksが立ち上げたコミュ二ティ『Deep Learning Lab』とは？ キックオフイベントレポート | Ledge.ai
MicrosoftとPreferred Networksが立ち上げたコミュ二ティ『Deep Learning Lab』とは？ キックオフイベントレポート 2017年5月23日、MicrosoftとPreferred Networksが、AIの実社会での活用促進を目的にディープラーニング分野における戦略的協業を発表しました。 さまざまなAI研究・AIソリューションでおなじみMicrosoftと、Chainer開発元でもあるPreferred Networks、この2社の協業発表は多くのニュースサイトでも取り上げられ、話題になっています。 そして、AI界隈で超有名なこの2社が立ち上げたコミュ二ティが『Deep Learning Lab』です。 でも、具体的に
しゅんけー on Twitter: "弊学大学院の名物授業，「エクセルでRecurrent Neural Networkを実装しよう」 https://t.co/ea7pNoGfpX"
弊学大学院の名物授業，「エクセルでRecurrent Neural Networkを実装しよう」 https://t.co/ea7pNoGfpX
Stateful LSTM in Keras
The idea of this post is to provide a brief and clear understanding of the stateful mode, introduced for LSTM models in Keras. If you have ever typed the words lstm and stateful in Keras, you may have seen that a significant proportion of all the issues are related to a misunderstanding of people trying to use this stateful mode. This post attempts to give insight to users on how to use for their
Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks
Cardiologist-Level Arrhythmia Detection With Convolutional Neural NetworksPranav Rajpurkar*, Awni Hannun*, Masoumeh Haghpanahi, Codie Bourn, and Andrew NgA collaboration between Stanford University and iRhythm Technologies We develop a model which can diagnose irregular heart rhythms, also known as arrhythmias, from single-lead ECG signals better than a cardiologist.Key to exceeding expert perform
単一画像からの最新ディープラーニング顔アルゴリズムを試す(2017 年 7 月 14 日時点) (2020 年 1 月 1 日追記) - Qiita
漢なら, 単一画像から顔認識したり顔メッシュを復元したりをディープラーニングでやりたいですね! やりましょう! DenseReg DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild https://arxiv.org/abs/1612.01202 顔のセグメンテーション(目, 鼻, 眉とか)と形状推定ができます. Semantic Image Segmentation 用に DeepLab http://liangchiehchen.com/projects/DeepLab.html を使っています. 独自 python caffe layer を使っているので, コンパイルするときには注意です! として python caffe module をビルドすることも忘れずに. ねんがんの DenseReg: Ful
Deep Learningでヒアリを検出・判定する(YOLO9000) - algonote
“殺人アリ"ことヒアリが日本に上陸したらしい。 Twitterではヒアリかどうかを判別してくれるヒアリ警察が人気だが、画像認識ではなく、中身は人間の専門家のようだ。ヒアリ警察さんも24時間365日稼働できないと思うので、Deep Learningで実装できないのか模索してみる。 先行事例 IBM Watsonはデフォルトでfire ant(ヒアリ)に対応。 dotnsf.blog.jp ただ、ヒアリ警察さんで紹介されている代表画像をかませてみたところ、全てのアリを識別できるわけではなかった。 アリのデータセットAntWebというのもあるらしい upura.hatenablog.com サイトのつくりが少し見辛い。 ざっとみた感じあくまで掲載論文のサーチが主眼のようなので、画像は少なめ。 IMAGENETにもfire antカテゴリあり Fire ant こちらは画像が豊富にある他、クモ等、
Deep Learning with Docker - Affine Layer
Docker is a way to statically link everything short of the Linux kernel into your application. Because you can access GPUs while using a Docker container, it's also a great way to link Tensorflow or any dependencies your machine learning code has so anyone can use your work. You can distribute a reproducible machine learning project that requires little to no setup on the part of the user, so inst
Perceptual Adversarial Networks for Image-to-Image Transformation
In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The prop
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A convolutional neural network approach for acoustic scene classification