Building a Real-Time Object Recognition App with Tensorflow and OpenCV (With nice multithread OpenCV -> TF pipeline for efficient I/O)
https://medium.com/towards-data-science/building-a-real-time-object-recognition-app-with-tensorflow-and-opencv-b7a2b4ebdc32
https://medium.com/towards-data-science/building-a-real-time-object-recognition-app-with-tensorflow-and-opencv-b7a2b4ebdc32
Medium
Building a Real-Time Object Recognition App with Tensorflow and OpenCV
In this article, I will walk through the steps how you can easily build your own real-time object recognition application with Tensorflow’s…
Recommending GitHub Repositories with Google BigQuery and the implicit library
https://medium.com/towards-data-science/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77
https://medium.com/towards-data-science/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77
Medium
Recommending GitHub Repositories with Google BigQuery and the implicit library
Keeping track of all the great repositories that are published in GitHub is an impossible task. The trending list does not help much. As…
Keras-vis: Toolkit to perform guided backprop for neural network visualizations
https://github.com/raghakot/keras-vis
https://github.com/raghakot/keras-vis
GitHub
GitHub - raghakot/keras-vis: Neural network visualization toolkit for keras
Neural network visualization toolkit for keras. Contribute to raghakot/keras-vis development by creating an account on GitHub.
Implementation of Sparse Variational Dropout
https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn
https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn
GitHub
senya-ashukha/variational-dropout-sparsifies-dnn
Sparse Variational Dropout, ICML 2017. Contribute to senya-ashukha/variational-dropout-sparsifies-dnn development by creating an account on GitHub.
Автоэнкодеры в Keras, Часть 4: Conditional VAE
https://habrahabr.ru/post/331664/
https://habrahabr.ru/post/331664/
Habr
Автоэнкодеры в Keras, Часть 4: Conditional VAE
Содержание Часть 1: Введение Часть 2: Manifold learning и скрытые ( latent ) переменные Часть 3: Вариационные автоэнкодеры ( VAE ) Часть 4: Conditional VAE Часть 5: GAN (Generative Adversarial...
How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3
https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3
Medium
How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
How we beat the state of the art to build a real-life A.I. app.
Why I’m Remaking OpenAI Universe
https://blog.aqnichol.com/2017/06/11/why-im-remaking-openai-universe/
https://blog.aqnichol.com/2017/06/11/why-im-remaking-openai-universe/
Draw Together with a Neural Network
https://aiexperiments.withgoogle.com/sketch-rnn-demo
https://aiexperiments.withgoogle.com/sketch-rnn-demo
A.I. Experiments
Sketch-RNN Demos - A.I. Experiments
This experiment lets you draw together with a recurrent neural network model called Sketch-RNN. We taught this neural net to draw by training it on millions of doodles collected from the Quick, Draw! game. Once you start drawing an object, Sketch-RNN will…
Как HBO делала приложение Not Hotdog для сериала «Кремниевая долина»
https://habrahabr.ru/post/331740/
https://habrahabr.ru/post/331740/
Habr
Как HBO делала приложение Not Hotdog для сериала «Кремниевая долина»
Сериал HBO «Кремниевая долина» выпустил настоящее приложение ИИ, которое распознаёт хотдоги и не-хотдоги, как приложение в четвёртом эпизоде четвёртогого сезона (приложение сейчас доступно для...
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation (in ECCV'16)
http://sites.skoltech.ru/compvision/projects/deepwarp/
http://sites.skoltech.ru/compvision/projects/deepwarp/
Tensorflow implementation of Deepmind Interaction Networks for Learning about Objects, Relations and Physics
https://github.com/jaesik817/Interaction-networks_tensorflow
https://github.com/jaesik817/Interaction-networks_tensorflow
GitHub
jaesik817/Interaction-networks_tensorflow
Interaction-networks_tensorflow - Tensorflow Implementation of Interaction Networks for Learning about Objects, Relations and Physics
[Discussion] Read-through: Hyperparameter Optimization: A Spectral Approach
http://www.alexirpan.com/2017/06/27/hyperparam-spectral.html
http://www.alexirpan.com/2017/06/27/hyperparam-spectral.html
Alexirpan
Read-through: Hyperparameter Optimization: A Spectral Approach
Similar to Wasserstein GAN,
this is another theory-motivated paper with neat
applications to deep learning. Once again, if you are looking for proof
details, you are better off reading the original paper. The goal
of this post is to give background and motivation.
this is another theory-motivated paper with neat
applications to deep learning. Once again, if you are looking for proof
details, you are better off reading the original paper. The goal
of this post is to give background and motivation.
Dynamic routing in artificial neural networks (ICML2017)
https://www.youtube.com/watch?v=NHQsDaycwyQ&feature=youtu.be
https://www.youtube.com/watch?v=NHQsDaycwyQ&feature=youtu.be
YouTube
Dynamic Routing in Artificial Neural Networks (Video Abstract)
[Project Resources] ICML 2017 Paper (Preprint): https://arxiv.org/abs/1703.06217 Poster: https://www.dropbox.com/s/svh5610fpfh7np1/drann-poster.pdf?dl=0 Soft...
Two ways to improve model accuracy and reduce training time -Explained
https://hackernoon.com/training-your-deep-model-faster-and-sharper-e85076c3b047
https://hackernoon.com/training-your-deep-model-faster-and-sharper-e85076c3b047
Hackernoon
Train your deep model faster and sharper — two novel techniques
A short Conditional DCGAN tensorflow implementation
https://github.com/Eyyub/tensorflow-cdcgan
https://github.com/Eyyub/tensorflow-cdcgan
GitHub
Eyyub/tensorflow-cdcgan
tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.
Zero to One — A Ton of Awe-Inspiring Deep Learning Demos with Code for Beginners
https://medium.com/@SamPutnam/deep-learning-download-and-run-a9a1e374d2d9
https://medium.com/@SamPutnam/deep-learning-download-and-run-a9a1e374d2d9
Medium
Zero to One — A Ton of Awe-Inspiring Deep Learning Demos with Code for Beginners
Check it out!
How to actually build a neural network from blocks? - with notMNIST in Keras [webinar]
https://www.crowdcast.io/e/neural-network-blocks/register
https://www.crowdcast.io/e/neural-network-blocks/register
Crowdcast
How to actually build a neural network from blocks? - Crowdcast
Deep learning is about creating machine learning models from compossible blocks, called layers. In this webinar you will learn convolutional neural network architectures for image classification.