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Convolutional Neural Network for Sentence Classification
The goal of a Knowledge Base–supported Question Answering (KB-supported QA) system is to answer a query natural language by obtaining the answer from a knowledge database, which stores knowledge in the form of (entity, relation, value) triples. QA systems understand questions by extracting entity and relation pairs. This thesis aims at recognizing the relation candidates inside a question. We defi
Implementing a Neural Network from Scratch in Python
All the code is also available as an Jupyter notebook on Github. In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details. Here I’m assuming that you are familiar with basic Calculus and Machine Learnin
Convolutional Neural Network (CNN) for Audio
I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to understand and follow. I want to extend the same CNN to extract multi-modal features from videos (images + audio) at the same time. I understand that video input is nothing but a sequence of images (pixel inten
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Recursive Neural Network の訓練 (Backpropagation Through Structure)
Recursive Neural Network の訓練 (Backpropagation Through Structure) Recursive neural network (RNN) は， 構文木の意味を表現する特徴ベクトルを計算するために使用されるモデルである． 歴史的には，主に自然言語処理において利用されてきたが， プログラミング言語の意味の解析とかにも使える [Li14]（詳細は， 1月2日の記事を参照のこと）． 今日は，RNN の訓練に用いられる backpropagation through structure (BPTS) [Go96] というアルゴリズムについて紹介する． Recursive neural network (RNN) RNN では，子を表現する特徴ベクトルを用いて，親を表現する特徴ベクトルを計算する， という処理を再帰的に繰り返すことで，根に対応する特
Chainerで書いたConvolutional Neural Network。MNISTの分類タスク。 · GitHub
train_mnist_cnn.py #coding: utf-8 import numpy as np import chainer from chainer import cuda import chainer.functions as F from chainer import optimizers import time from sklearn.datasets import fetch_mldata from sklearn.cross_validation import train_test_split import pylab import matplotlib.pyplot as plt gpu_flag = 0 if gpu_flag >= 0: cuda.check_cuda_available() xp = cuda.cupy if gpu_flag >= 0 el
DeepGrammar: Grammar Checking Using Deep Learning
I make a lot of dumb mistakes when I write, and I’ve always dreamed of having a smart computer that could point out the errors that escape my notice. Building such a grammar checker is hard. You can’t just write down the rules of English grammar and check that they are followed like you can when building a compiler for a programming language. Natural languages such as English have some syntactic r
Neural Network Matrix Factorization
Data often comes in the form of an array or matrix. Matrix factorization techniques attempt to recover missing or corrupted entries by assuming that the matrix can be written as the product of two low-rank matrices. In other words, matrix factorization approximates the entries of the matrix by a simple, fixed function---namely, the inner product---acting on the latent feature vectors for the corre
IBM Watson は AlchemyAPI社 買収 で得た Deep Learning 技術 を使って、知識データベース の 拡張（学習）自動化 を実現した？ - Qiita
2015年3月16日、IBM社 が AlchemyAPI社 買収を発表した。 この買収の狙いが、Deep Learning がもつ データから 概念 や オブジェクト を自動的に抽出•識別するスキルを、Watson の知識ベース 拡張プロセス（新規取り込みデータの逐次学習による 知識＝「事実」と「ルール」の自動拡張）に取り組む ことにあるならば、これは画期的だ、と思い、買収目的について同社のプレスリリースにあたってみた。 プレスリリース（日本語訳） には、期待どおり、次の文章が書きこまれていた。 「大規模なデータ・セット内の階層をすばやく識別して関係を理解するWatsonの能力強化」 「一般的な事業や業界固有のさまざまなデータを幅広く取り込み」、「トレーニング、学習する能力」や、「絶えず進化するオントロジー（概念定義や体系化）を管理するニーズに対処できる能力といったWatsonの能力向上に
How to trick a neural network into thinking a panda is a vulture
How to trick a neural network into thinking a panda is a vulture Julia Evans Neural networks are magical When I go to Google Photos and search my photos for ‘skyline’, it finds me this picture of the New York skyline I took in August, without me having labelled it! When I search for ‘cathedral’, Google’s neural networks find me pictures of cathedrals & churches I’ve seen. It seems magical. But of
Deep Learning | Udacity
Dive into deep learning with this practical course on TensorFlow and the Keras API. Gain an intuitive understanding of neural networks without the dense jargon. Learn to build, train, and optimize your own networks using TensorFlow. The course also introduces transfer learning, leveraging pre-trained models for enhanced performance. Designed for swift proficiency, this course prioritizes hands-on
話題のディープラーニングを総括する特別イベントを2月9日に開催
深層学習と呼ばれる「ディープラーニング」を中心に、これら最新テクノロジーを総括するイベントをオープンソースビジネス推進協議会の主催で開催することになりました。 昨年からにわかに注目されている機械学習や人工知能といった新たなテクノロジー。Think ITでもディープラーニングに関する連載を開始し多くのアクセスを記録しました。 これらの新しい技術は、決してちょっと先の未来などではなく、すでに実用レベルでビジネスに活用されているものであることをまずは認識する必要があると思います。人工知能を手軽に活用できるWatson APIや、機械学習環境をクラウドで提供するAzure Machine Learningなど、すでに実サービスとして展開が進みつつあります（これらは2016年さらに増えていくことが予想されます）。今回、深層学習と呼ばれる「ディープラーニング」を中心に、これら最新テクノロジーを総括する
Which whale is it, anyway? Face recognition for right whales using deep learning - deepsense.ai
The challenge The goal of the competition was to recognize individual right whales in photographs taken during aerial surveys. When visualizing the scenario, do not forget that these giants grow up to more than 18 metres, and weigh up to 91 tons. There were 447 different right whales in the data set (and this is likely the overall number of living specimens). Even though that number is (terrifying
Caffe | Siamese Network Tutorial
Siamese Network Training with Caffe This example shows how you can use weight sharing and a contrastive loss function to learn a model using a siamese network in Caffe. We will assume that you have caffe successfully compiled. If not, please refer to the Installation page. This example builds on the MNIST tutorial so it would be a good idea to read that before continuing. The guide specifies all p
Caffe | Deep Learning Framework
Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Why Caffe? Expressive architecture encourages application and innovat
Caffe | Caffe Tutorial
Caffe Tutorial Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. While explanations will be given where possible, a background in machine learning and neural networks is helpful. Philosophy In one sip, Caffe i
Caffe | Installation
Installation Prior to installing, have a glance through this guide and take note of the details for your platform. We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS. The official Makefile and Makefile.config build are complemented by a community CMake build. Step-by-step Instructions: Docker setup out-of-the-box brewing Ubuntu installation the standard pla
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Convolutional neural network - Wikipedia
