Displaying 1 to 20 from 102 results

mkl-dnn - Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)

  •    C++

Intel MKL-DNN repository migrated to https://github.com/intel/mkl-dnn. The old address will continue to be available and will redirect to the new repo. Please update your links. Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

places365 - The Places365-CNNs for Scene Classification

  •    Python

We release various convolutional neural networks (CNNs) trained on Places365 to the public. Places365 is the latest subset of Places2 Database. There are two versions of Places365: Places365-Standard and Places365-Challenge. The train set of Places365-Standard has ~1.8 million images from 365 scene categories, where there are at most 5000 images per category. We have trained various baseline CNNs on the Places365-Standard and released them as below. Meanwhile, the train set of Places365-Challenge has extra 6.2 million images along with all the images of Places365-Standard (so totally ~8 million images), where there are at most 40,000 images per category. Places365-Challenge will be used for the Places2 Challenge 2016 to be held in conjunction with the ILSVRC and COCO joint workshop at ECCV 2016. The data Places365-Standard and Places365-Challenge are released at Places2 website.




tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference

  •    PureBasic

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

lstm-char-cnn-tensorflow - in progress

  •    Python

Tensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found here. The current implementation has a performance issue. See #3.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

  •    Jupyter

How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".


Awesome-Deep-Learning-Resources - Rough list of my favorite deep learning resources, useful for revisiting topics or for reference

  •    

This is a rough list of my favorite deep learning resources. It has been useful to me for learning how to do deep learning, I use it for revisiting topics or for reference. I (Guillaume Chevalier) have built this list and got through all of the content listed here, carefully. You might also want to look at Andrej Karpathy's new post about trends in Machine Learning research.

deepgaze - Computer Vision library for human-computer interaction

  •    Python

Update 04/06/2017 Article "Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods" have been accepted for publication in Pattern Recogntion (Elsevier). The Deepgaze CNN head pose estimator module is based on this work. Update 22/03/2017 Fixed a bug in mask_analysis.py and almost completed a more robust version of the CNN head pose estimator.

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

  •    Python

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

Screenshot-to-code-in-Keras - A neural network that transforms a screenshot into a static website

  •    Jupyter

This is the code for the article 'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit ่Žซ็ƒฆ Python for more.

srgan - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

  •    Python

We run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. ๐Ÿš€ This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.

TensorFlow-Tutorials - ํ…์„œํ”Œ๋กœ์šฐ๋ฅผ ๊ธฐ์ดˆ๋ถ€ํ„ฐ ์‘์šฉ๊นŒ์ง€ ๋‹จ๊ณ„๋ณ„๋กœ ์—ฐ์Šตํ•  ์ˆ˜ ์žˆ๋Š” ์†Œ์Šค ์ฝ”๋“œ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค

  •    Python

ํ…์„œํ”Œ๋กœ์šฐ๋ฅผ ๊ธฐ์ดˆ๋ถ€ํ„ฐ ์‘์šฉ๊นŒ์ง€ ๋‹จ๊ณ„๋ณ„๋กœ ์—ฐ์Šตํ•  ์ˆ˜ ์žˆ๋Š” ์†Œ์Šค ์ฝ”๋“œ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ํ…์„œํ”Œ๋กœ์šฐ ๊ณต์‹ ์‚ฌ์ดํŠธ์—์„œ ์ œ๊ณตํ•˜๋Š” ์•ˆ๋‚ด์„œ์˜ ๋Œ€๋ถ€๋ถ„์˜ ๋‚ด์šฉ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ์œผ๋ฉฐ, ๊ณต์‹ ์‚ฌ์ดํŠธ์—์„œ ์ œ๊ณตํ•˜๋Š” ์†Œ์Šค ์ฝ”๋“œ๋ณด๋‹ค๋Š” ํ›จ์”ฌ ๊ฐ„๋žตํ•˜๊ฒŒ ์ž‘์„ฑํ•˜์˜€์œผ๋ฏ€๋กœ ์‰ฝ๊ฒŒ ๊ฐœ๋…์„ ์ตํž ์ˆ˜ ์žˆ์„ ๊ฒƒ ์ž…๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ชจ๋“  ์ฃผ์„์€ ํ•œ๊ธ€๋กœ(!) ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.

PyCNN - Image Processing with Cellular Neural Networks in Python

  •    Python

Cellular Neural Networks (CNN) [wikipedia] [paper] are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. This python library is the implementation of CNN for the application of Image Processing.





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