tensorflow-tutorial - TensorFlow and Deep Learning Tutorials

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Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnn

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

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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 https://www.kaggle.com/c/ultrasound-nerve-segmentation. 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.

image-segmentation keras medical-imaging deep-neural-networks convolutional-networks

regl-cnn - Digit recognition with Convolutional Neural Networks in WebGL

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GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

regl cnn digit-recognition demo gpu webgl convolutional-neural-networks gpgpu deep-learning glsl digit recognition mnist convolutional neural network networks

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

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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".

keras cnn cifar10 machine-learning tensorflow deep-learning neural-network imagenet image-processing nlp

artistic-style-transfer - Convolutional neural networks for artistic style transfer.

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This repository contains (TensorFlow and Keras) code that goes along with a related blog post and talk (PDF). Together, they act as a systematic look at convolutional neural networks from theory to practice, using artistic style transfer as a motivating example. The blog post provides context and covers the underlying theory, while working through the Jupyter notebooks in this repository offers a more hands-on learning experience. If you have any questions about any of this stuff, feel free to open an issue or tweet at me: @copingbear.

convolutional-neural-networks tutorial-code talk tensorflow keras

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

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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.

deep-learning theano tensorflow cntk object-detection image-segmentation

food-101-keras - Food Classification with Deep Learning in Keras / Tensorflow

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If you are reading this on GitHub, the demo looks like this. Please follow the link below to view the live demo on my blog. Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.

deep-learning image-classification ai machine-learning food-classification keras tensorflow

t81_558_deep_learning - Washington University (in St

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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.

neural-network machine-learning tensorflow keras deeplearning

CNN-for-Sentence-Classification-in-Keras - Convolutional Neural Networks for Sentence Classification in Keras

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Convolutional Neural Networks for Sentence Classification in Keras

nmt-keras - Neural Machine Translation with Keras (Theano/Tensorflow)

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Neural Machine Translation with Keras (Theano and Tensorflow). for obtaining the required packages for running this library.

neural-machine-translation keras deep-learning sequence-to-sequence theano machine-learning nmt machine-translation lstm-networks gru tensorflow attention-mechanism web-demo transformer attention-is-all-you-need attention-model attention-seq2seq

nn_playground - Experimental keras implementation of novel neural network structures

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Experimental keras implementation of novel neural network structures

deep-learning deep-neural-networks neural-networks keras

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

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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.

deep-learning tensorflow theano neural-networks machine-learning data-science

crfasrnn_keras - CRF-RNN Keras/Tensorflow version

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This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

image-segmentation semantic-segmentation crf-as-rnn tensorflow keras crfasrnn crfasrnn-keras crfasrnn-tensorflow crf-rnn-tensorflow crf-rnn-keras

Tensorflow-Programs-and-Tutorials - Implementations of CNNs, RNNs, GANs, etc

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CNN's with Noisy Labels - This notebook looks at a recent paper that discusses how convolutional neural networks that are trained on random labels (with some probability) are still able to acheive good accuracy on MNIST. I thought that the paper showed some eye-brow raising results, so I went ahead and tried it out for myself. It was pretty amazing to see that even when training a CNN with random labels 50% of the time, and the correct labels the other 50% of the time, the network was still able to get a 90+% accuracy. Character Level RNN (Work in Progress) - This notebook shows you how to train a character level RNN in Tensorflow. The idea was inspired by Andrej Karpathy's famous blog post and was based on this Keras implementation. In this notebook, you'll learn more about what the model is doing, and how you can input your own dataset, and train a model to generate similar looking text.

tensorflow deep-learning machine-learning

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph

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A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network

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

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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.

outlier-detection anomaly-detection outlier-ensembles outliers anomaly machine-learning data-mining unsupervised-learning python2 python3 fraud-detection autoencoder neural-networks deep-learning

MobileNet-CoreML - The MobileNet neural network using Apple's new CoreML framework

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This is the MobileNet neural network architecture from the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications implemented using Apple's shiny new CoreML framework. This uses the pretrained weights from shicai/MobileNet-Caffe.

core-ml machine-learning mobilenet ios

SRCNNKit - CoreML and Keras implementation of Super-Resolution Convolutional Neural Network (SRCNN)

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Implementation of Super Resolution (SR) with CoreML and Swift. You can use SR method in your app using SRCNNKit UIImageView extension. Sorry, this project dosen't contain *.mlmodel yet. You should train your own model and import SRCNN.mlmodel to your project.

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

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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.

jupyter-notebook keras deep-learning colorization tensorflow fusion-layer floydhub

Keras-GAN - Keras implementations of Generative Adversarial Networks.

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Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GAN varieties to implement are very welcomed. Implementation of Auxiliary Classifier Generative Adversarial Network.

deep-learning gan keras generative-adversarial-networks neural-networks