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
Caffe | Model Zoo
Caffe Model Zoo Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the model zoo! These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications. To help share these models, we introduce
Caffe | Installation: RHEL / Fedora / CentOS
# glog wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz tar zxvf glog-0.3.3.tar.gz cd glog-0.3.3 ./configure make && make install # gflags wget https://github.com/schuhschuh/gflags/archive/master.zip unzip master.zip cd gflags-master mkdir build && cd build export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 make && make install #
Caffe | Solver / Model Optimization
Solver The solver orchestrates model optimization by coordinating the network’s forward inference and backward gradients to form parameter updates that attempt to improve the loss. The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients. The Caffe solvers are: Stochastic Gradient De
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversa
GitHub - kjw0612/awesome-rnn: Recurrent Neural Network - A curated list of resources dedicated to RNN
Tensorflow - Python, C++ Get started, Tutorials Recurrent Neural Network Tutorial Sequence-to-Sequence Model Tutorial Tutorials by nlintz Notebook examples by aymericdamien Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow Keras : (Tensorflow / Theano)-based modular deep learning library similar to Torch char-rnn-tensorflow by sherjilozair: char-rnn in tensorflow Theano
A Neural Network in 11 lines of Python (Part 1) - i am trask
A bare bones neural network implementation to describe the inner workings of backpropagation. Posted by iamtrask on July 12, 2015 Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Edit: Some folks have asked about a followup article, and I'm planning to write one. I'll tweet it out when it's
ディープラーニング - Wikipedia
英語版記事を日本語へ機械翻訳したバージョン（Google翻訳）。 万が一翻訳の手がかりとして機械翻訳を用いた場合、翻訳者は必ず翻訳元原文を参照して機械翻訳の誤りを訂正し、正確な翻訳にしなければなりません。これが成されていない場合、記事は削除の方針G-3に基づき、削除される可能性があります。 信頼性が低いまたは低品質な文章を翻訳しないでください。もし可能ならば、文章を他言語版記事に示された文献で正しいかどうかを確認してください。 履歴継承を行うため、要約欄に翻訳元となった記事のページ名・版について記述する必要があります。記述方法については、Wikipedia:翻訳のガイドライン#要約欄への記入を参照ください。 翻訳後、{{翻訳告知|en|Deep learning|…}}をノートに追加することもできます。 Wikipedia:翻訳のガイドラインに、より詳細な翻訳の手順・指針についての説明があ
【機械学習】ディープラーニング フレームワークChainerを試しながら解説してみる。 - Qiita
今話題のDeep Learning(深層学習)フレームワーク、Chainerに手書き文字の判別を行うサンプルコードがあります。こちらを使って内容を少し解説する記事を書いてみたいと思います。 (本記事のコードの全文をGitHubにアップしました。[PC推奨]) とにかく、インストールがすごく簡単かつ、Pythonが書ければすぐに使うことができておすすめです！ Pythonに閉じてコードが書けるのもすごくいいですよね。 こんな感じのニューラルネットワークモデルを試してみる、という記事です。 主要な情報はこちらにあります。 Chainerのメインサイト ChainerのGitHubリポジトリ Chainerのチュートリアルとリファレンス 1. インストール# まずは何はともあれインストールです。ChainerのGitHubに記載の"Requirements" ( https://github.c
画像認識の性能は圧倒的、他領域ではまだ発展途上
一口にディープラーニング技術といっても、用いられるニューラルネットワークの構造や手法はさまざまだ。画像認識や音声認識、自然言語処理、ロボット制御などタスクごとに使われ方は異なる。ここではそうしたタスクごとにディープラーニング技術の現状を見ていく。 ディープラーニング技術のブームの火付け役となり、最も成果が上がっているのが画像認識である。2012年に「ILSVRC（ImageNet large scale visual recognition challenge）」という画像認識のコンテストで、ディープラーニング技術を用いたチームが既存手法を用いたチームに10％以上の大差を付けて優勝。以来、ディープラーニング技術は世界的な注目を集めるようになった。 ディープラーニング技術を画像認識に用いる場合、現在では「CNN（convolutional neural network）」という構造を用いるこ
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
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Caffe | Interfaces
https://rest-term.com/archives/3269/
Deeplearning 4 j のクイックスタートガイド - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
