A Bayesian Perspective on Generalization and Stochastic Gradient Descent | Deep Learning JP
MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
I trained a recurrent neural network to play Mario Kart human-style. MariFlow Manual & Download: https://docs.google.com/document/d/1p4ZOtziLmhf0jPbZTTaFxSKdYqE91dYcTNqTVdd6es4/edit?usp=sharing Mushroom Cup: https://www.twitch.tv/videos/183296063 Flower Cup: https://www.twitch.tv/videos/183296268 Star Cup: https://www.twitch.tv/videos/183296400 SethBling Twitter: http://twitter.com/sethbling Set
Backpropagation, intuitively | Deep Learning Chapter 3
What's actually happening to a neural network as it learns? Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: http://3b1b.co/nn3-thanks Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks And by CrowdFlower: http://3b1b.co/crowdflower
Gophercises
Gophercises is free, but you need to provide a working email address to gain access. I won't spam you and unsubscribing is very easy. We've all been there before... You are just starting to pick up a new programming language (like Go!) and things are going great. The tutorials are clicking and you are making great progress... and then you run out of tutorials. "What should I build next?" you ask,
Pyro
NumPyro Release We’re excited to announce the release of NumPyro, a NumPy-backed Pyro using JAX for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the examples and documentation for more details. Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive de
Fooling Neural Networks in the Physical World
Fooling Neural Networks in the Physical World with 3D Adversarial Objects We’ve developed an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at. Neural network based classifiers reach near-human performance in many tasks, and they’re used in high risk, real world systems. Yet, these same neural networks are part
The Devil Is in the Detail of Deep Learning Hardware
To identify skin cancer, perceive human speech, and run other deep learning tasks, chipmakers are editing processors to work with lower precision numbers. To identify skin cancer, perceive human speech, and run other deep learning tasks, chipmakers are editing processors to work with lower precision numbers. These numbers contain fewer bits than those with higher precision, which require heavier l
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks witho
How to colorize black & white photos with just 100 lines of neural network code
By Emil Wallner 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 i
これから強化学習を勉強する人のための「強化学習アルゴリズム・マップ」と、実装例まとめ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ※2018年06月23日追記 PyTorchを使用した最新版の内容を次の書籍にまとめました。 つくりながら学ぶ! 深層強化学習 ~PyTorchによる実践プログラミング~ 18年6月28日発売 これから強化学習を勉強したい人に向けて、「どんなアルゴリズムがあるのか」、「どの順番で勉強すれば良いのか」を示した強化学習アルゴリズムの「学習マップ」を作成しました。 さらに、各手法を実際にどう実装すれば良いのかを、簡単な例題を対象に実装しました。 本記事では、ひとつずつ解説します。 オレンジ枠の手法は、実装例を紹介します。 ※今回マップを作るに
mixup: Beyond Empirical Risk Minimization
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear
A multi-branch convolutional neural network for detecting double JPEG compression
Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs from pre-processed data, such as histogram features and/or decompressed images. In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input. Consider
Announcing New AWS Deep Learning AMI for Amazon EC2 P3 Instances
The new AMIs are pre-installed and configured with CUDA 9 for both Ubuntu and Amazon Linux, along with the other GPU drivers to take advantage of the speed of Volta on P3, including cuDNN 7.0, NCCL 2.0.5 and the NVIDIA Driver 384.81. In addition to the latest drivers, the AMI also includes popular frameworks which have been optimized for P3 and Volta including Apache MXNet v0.12 RC1, Caffe2 v0.8.1
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GitHub - GU-CLASP/TypedFlow: Typed frontend to TensorFlow and higher-order deep learning
ソニー、生まれ変わった新「aibo」を発表 ～2018年1月11日に発売。先行予約は11月1日23時1分から
GitHub - astorfi/CNTK-World: :earth_americas: Simple and ready-to-use deep learning examples for the Microsoft Cognitive Toolkit (CNTK)
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GitHub - onnx/onnx: Open standard for machine learning interoperability
