nets - Welcome to the Academy of Arts

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You don't have to think about filter size, strides and activation function. It is all about shapes, colors and fonts.



Related Projects

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

t81_558_deep_learning - Washington University (in St

  •    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

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".

MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks

  •    Python

A comprehensive, cross-framework solution to convert, visualize and diagnosis deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.Across the industry and academia, there are a number of existing frameworks available for developers and researchers to design a model, where each framework has its own network structure definition and saving model format. The gaps between frameworks impede the inter-operation of the models.

robot-surgery-segmentation - Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge

  •    Jupyter

Here we present our wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge. In this work, we describe our winning solution for MICCAI 2017 Endoscopic Vision Sub-Challenge: Robotic Instrument Segmentation and demonstrate further improvement over that result. Our approach is originally based on U-Net network architecture that we improved using state-of-the-art semantic segmentation neural networks known as LinkNet and TernausNet. Our results shows superior performance for a binary as well as for multi-class robotic instrument segmentation. We believe that our methods can lay a good foundation for the tracking and pose estimation in the vicinity of surgical scenes.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

keras-rl - Deep Reinforcement Learning for Keras.

  •    Python

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

CNTK - Computational Network Toolkit (CNTK)

  •    C++

The Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. It is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

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.

keras-rcnn - Keras package for region-based convolutional neural networks (RCNNs)

  •    Python

keras-rcnn is the Keras package for region-based convolutional neural networks. The data is made up of a list of dictionaries corresponding to images.

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

  •    Python

Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

Coloring-greyscale-images-in-Keras - Coloring B&W portraits with neural networks.

  •    Jupyter

Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir’s neural network, so I reproduced it and documented the process. Read the article to understand the context of the code.

pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

  •    Python

Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

ngraph - nGraph is an open source C++ library, compiler and runtime for Deep Learning frameworks

  •    C++

Welcome to the open-source repository for the Intel® nGraph™ Library. Our code base provides a Compiler and runtime suite of tools (APIs) designed to give developers maximum flexibility for their software design, allowing them to create or customize a scalable solution using any framework while also avoiding device-level hardware lock-in that is so common with many AI vendors. A neural network model compiled with nGraph can run on any of our currently-supported backends, and it will be able to run on any backends we support in the future with minimal disruption to your model. With nGraph, you can co-evolve your software and hardware's capabilities to stay at the forefront of your industry. The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks. Documentation in this repo describes how you can program any framework to run training and inference computations on a variety of Backends including Intel® Architecture Processors (CPUs), Intel® Nervana™ Neural Network Processors (NNPs), cuDNN-compatible graphics cards (GPUs), custom VPUs like Movidius, and many others. The default CPU Backend also provides an interactive Interpreter mode that can be used to zero in on a DL model and create custom nGraph optimizations that can be used to further accelerate training or inference, in whatever scenario you need.

MMLSpark - Microsoft Machine Learning for Apache Spark

  •    Scala

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

seq2seq - Sequence to Sequence Learning with Keras

  •    Python

Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python deep learning library Keras. Using Seq2Seq, you can build and train sequence-to-sequence neural network models in Keras. Such models are useful for machine translation, chatbots (see [4]), parsers, or whatever that comes to your mind. That's it! You have successfully compiled a minimal Seq2Seq model! Next, let's build a 6 layer deep Seq2Seq model (3 layers for encoding, 3 layers for decoding).

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

  •    Python

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.