問題解決のための「アルゴリズム × 数学」- Forkwell Library #1
📝 イベントレポートも発信しています 📝 コチラから：https://pr.forkwell.com/events/forkwell-library-01/ ⏳ プログラム 0:00 待ち時間 4:59 オープニング 9:08 基調講演「30 分でわかる！アルゴリズムの基本」『問題解決のための「アルゴリズム×数学」が基礎からしっかり身につく本』著者 米田 優峻 氏 45:12『問題解決のための「アルゴリズム×数学」が基礎からしっかり身につく本』プレゼント企画のご案内 52:41 視聴者Q&Aパネルトーク「アプリケーション開発で活かせること」shisamaさん × 米田 優峻さん 🐦 気づき・感想をシェアしてね 参加者全員で、この勉強会からの気づきを最大化しましょう！ 以下の Twitter ハッシュタグでツイートをお願いします。 #Forkwell_Library URL：htt
Hidetoshi Nishimori: "Theory of Quantum Annealing"
Hidetoshi Nishimori visited Google LA on March 28, 2014 to give a talk: "Theory of Quantum Annealing" Abstract: Quantum annealing is a generic framework, metaheuristic, for combinatorial optimization. I will first review the basic formulation of quantum annealing and numerical evidence for its performance, particularly in comparison with classical simulated annealing. I will then explain a few
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
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
Quick-sort with Hungarian (Küküllőmenti legényes) folk dance
Created at Sapientia University, Tirgu Mures (Marosvásárhely), Romania. Directed by Kátai Zoltán and Tóth László. In cooperation with "Maros Művészegyüttes", Tirgu Mures (Marosvásárhely), Romania. Choreographer: Füzesi Albert. Video: Lőrinc Lajos, Körmöcki Zoltán. Supported by "Szülőföld Alap", MITIS (NGO) and evoline company. Click the link below to watch this visualization included in the
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
15 Sorting Algorithms in 6 Minutes
Visualization and "audibilization" of 15 Sorting Algorithms in 6 Minutes. Sorts random shuffles of integers, with both speed and the number of items adapted to each algorithm's complexity. The algorithms are: selection sort, insertion sort, quick sort, merge sort, heap sort, radix sort (LSD), radix sort (MSD), std::sort (intro sort), std::stable_sort (adaptive merge sort), shell sort, bubble sort,
Tetris Printer Algorithm
By rotating, positioning and dropping a predetermined sequence of pieces, the Tetris Printer Algorithm exploits the mechanics of Tetris to generate arbitrary bitmap images. For a full discussion of the algorithm, see the link below. http://meatfighter.com/tetrisprinteralgorithm/ Royalty Free Music Kick Shock Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 ht
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Tensorflow and deep learning, without a PhD, Martin Gorner, Google
Connections between physics and deep learning
The Sound of Quicksort
Pedestrian Detection by Deep Convolutional Neural Network
動画の中の人が何をしているか分かるアルゴリズム、開発される