Neural Network Console：ツールで体験する、新しいディープラーニング
学習・評価だけでなく、ニューラルネットワークの設計までも可能なディープラーニングツールです。 グラフィカルなユーザーインターフェイス(GUI)による直感的な操作で、ディープラーニングをはじめましょう。 詳しくは下記サイトをご覧ください。 アプリケーションのダウンロードも下記サイトより行うことができます。 https://dl.sony.com/ja/ より詳しいNeural Network Consoleの特長はこちら Deep Learningの統合開発環境 Neural Network Consoleの特長 (2019/02) https://youtu.be/y_KGyxAwAic Deep Learningを基礎から学びたい方はこちら 再生リスト「Deep Learning入門」 https://www.youtube.com/playlist?list=PLg1wtJlhf
Deep Learning and the Analysis of Satellite Imagery
Boris Babenko of Orbital Insight talks about Deep Learning and the Analysis of Satellite Imagery. Orbital Insight is a Geospatial Big Data company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales. In this talk Boris discusses the satellite imagery dom
Android Meets TensorFlow: How to Accelerate Your App with AI (Google I/O '17)
Portability is one of the main benefits of TensorFlow -- you can easily move a neural network model to Android and run predictions on mobile phones, for all kinds of AI tricks from image recognition to motion recognition. But models are often large (tens of megabytes) and prediction can consume lots of CPU power. In this session, we'll share tips and tricks on overcoming these challenges so you ca
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera - SIGGRAPH2017
We are looking for PhD students and postdocs. Check: http://gvv.mpi-inf.mpg.de/GVV_Offers.html We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutio
Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
Deep Reinforcement Learning (John Schulman, OpenAI)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
Asynchronous Methods for Deep Reinforcement Learning: TORCS
The video shows an agent driving a racecar using only raw pixels as input. The agent was trained using the Asynchronous Advantage Actor-Critic (A3C) algorithm. During training, the agent was rewarded for maintaining high velocity along the center of the racetrack. Paper link - http://arxiv.org/pdf/1602.01783.pdf
Neural Network Learns to Generate Voice (RNN/LSTM)
[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned. This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the
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Tensorflow and deep learning, without a PhD, Martin Gorner, Google
Connections between physics and deep learning
Pedestrian Detection by Deep Convolutional Neural Network