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
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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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
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
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
Caffe | Installation: Ubuntu
for CUDA version. Note, the cuda version may break if your NVIDIA driver and CUDA toolkit are not installed by APT. Package status of CPU-only version Package status of CUDA version Installing Caffe from source We may install the dependencies by merely one line sudo apt build-dep caffe-cpu # dependencies for CPU-only version sudo apt build-dep caffe-cuda # dependencies for CUDA version CUDA: Insta
Caffe: caffe Namespace Reference
A layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function: More... Enumeration of actions that a client of the Solver may request by implementing the Solver's action request function, which a client may optionally provide in order to request early termination or saving a snapshot without e
Caffe | Installation: OS X
OS X Installation We highly recommend using the Homebrew package manager. Ideally you could start from a clean /usr/local to avoid conflicts. In the following, we assume that you’re using Anaconda Python and Homebrew. CUDA: Install via the NVIDIA package that includes both CUDA and the bundled driver. CUDA 7 is strongly suggested. Older CUDA require libstdc++ while clang++ is the default compiler
Caffe | ImageNet tutorial
Brewing ImageNet This guide is meant to get you ready to train your own model on your own data. If you just want an ImageNet-trained network, then note that since training takes a lot of energy and we hate global warming, we provide the CaffeNet model trained as described below in the model zoo. Data Preparation The guide specifies all paths and assumes all commands are executed from the root caff
Caffe | LeNet MNIST Tutorial
Training LeNet on MNIST with Caffe We will assume that you have Caffe successfully compiled. If not, please refer to the Installation page. In this tutorial, we will assume that your Caffe installation is located at CAFFE_ROOT. Prepare Datasets You will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands: cd $CAFFE_ROOT ./data/mn
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Convolutional neural network - Wikipedia
Caffe | Interfaces