dataset | classes | Objects | Dimension |
---|---|---|---|
iris | 3 | 150 | 4 |
wine | 3 | 178 | 13 |
Libras Movement | 15 | 360 | 91 |
spam | 2 | 4601 | 57 |
CNAE-9 | 9 | 1080 | 857 |
dataset | classes | Objects | Dimension |
CIFAR-10 | 10 | 60000 (50000 training images and 10000 test images) | 32 * 32 |
Handwritten digits | 10 | 5620 | 8 * 8 |
Olivetti face images | 40 | 400 | 92 * 112 (64 * 64) |
MNIST | 10 | 60,000 (training) 10,000 (test) | 28 * 28 |
Stanford Dogs Dataset | 120 | 20580 | 375 * 50 |
STL10 | 10 | 500 training images, 800 test images per class | 96 * 96 |
bus | clock (GHz) | sm | CUDA cores | Tensor Cores | メモリGB | メモリ帯域GB/s | Single (Tflops) | Double (Gflops) | Half (Tflops) | TDP (W) | |
---|---|---|---|---|---|---|---|---|---|---|---|
RTX 3060 | PCIe3x16 | 1.32 (1.777) | 28 | 3584 | 112 | 12 | 360 | 12.74 | 199.0 | 12.74 | 170 |
RTX 3060Ti | PCIe3x16 | 1.41 (1.665) | 38 | 4864 | 152 | 8 | 448 | 16.2 | 263.1 | 16.2 | 200 |
RTX 3070 | PCIe3x16 | 1.5 (1.725) | 46 | 5888 | 184 | 8 | 448 | 20.31 | 317.4 | 20.31 | 220 |
RTX 3080 | PCIe3x16 | 1.44 (1,71) | 68 | 8704 | 272 | 10 | 760.3 | 29.77 | 465.1 | 29.77 | 320 |
RTX 3090 | PCIe3x16 | 1.395 (1.695) | 82 | 10496 | 328 | 24 | 936.2 | 35.58 | 556.0 | 35.58 | 350 |
bus | clock (MHz) | sm | CUDA cores | Tensor Cores | メモリGB | メモリ帯域GB/s | Single (Gflops) | Double (Gflops) | Half (Gflops) | TDP (W) | |
---|---|---|---|---|---|---|---|---|---|---|---|
GTX 1650 | PCIe3x16 | 1485 | 14 | 896 | - | 4 | 128 | 2661 | 83 | 5322 | 75 |
RTX 2060 | PCIe3x16 | 1365 | 30 | 1920 | 240 | 6 | 336 | 5242 | 164 | 10483 | 160 |
RTX 2060 Super | PCIe3x16 | 1470 | 34 | 2176 | 272 | 8 | 448 | 6123 | 191 | 12246 | 175 |
RTX 2070 | PCIe3x16 | 1410 | 36 | 2304 | 288 | 8 | 448 | 6497 | 203 | 12994 | 175 |
RTX 2070 Super | PCIe3x16 | 1605 | 40 | 2560 | 320 | 8 | 448 | 8218 | 257 | 16435 | 215 |
RTX 2080 | PCIe3x16 | 1515 | 46 | 2944 | 368 | 8 | 448 | 8920 | 279 | 17840 | 215 |
RTX 2080 Super | PCIe3x16 | 1650 | 48 | 3072 | 384 | 8 | 496 | 10138 | 317 | 20275 | 250 |
RTX 2080Ti | PCIe3x16 | 1350 | 68 | 4352 | 544 | 11 | 616 | 11750 | 367 | 23500 | 250 |
Titan RTX | PCIe3x16 | 1350 | 72 | 4608 | 576 | 24 | 672 | 12442 | 389 | 24884 | 280 |
bus | clock (MHz) | six, sm | CUDA cores | Tensor Cores | メモリGB | メモリ帯域GB/s | Single (Gflops) | Double (Gflops) | Half (Gflops) | TDP (W) | |
---|---|---|---|---|---|---|---|---|---|---|---|
K20c | PCIe2x16 | 706 | 13 | 2496 | - | 5 | 208 | 3524 | 1175 | - | 225 |
GTX750Ti | PCIe3x16 | 1020 | 5 | 640 | - | 2 | 86.4 | 1306 | 40.8 | - | 60 |
k2200 | PCIe2x16 | 1046 | 5 | 640 | - | 4 | 80 | 1300 | 40 | - | 68 |
GTX1050Ti | PCIe3x16 | 1290 | 6 | 768 | - | 4 | 112 | 1981 | 62 | - | 75 |
GTX1060 6G | PCIe3x16 | 1506 | 10 | 1280 | - | 6 | 192 | 3855 | 120 | - | 120 |
GTX1080 | PCIe3x16 | 1607 | 20 | 2560 | - | 8 | 320 | 8228 | 257 | - | 180 |
GTX1080Ti | PCIe3x16 | 1480 | 28 | 3584 | - | 11 | 484 | 10609 | 332 | - | 250 |
Titan V | PCIe3x16 | 1200 | 80 | 5120 | 640 | 12 | 652.8 | 12288 | 6144 | 24576 | 250 |
年 | 機械学習 | 人工ニューラルネットワーク | その他 |
---|---|---|---|
1700s | ベイズ定理(Bayes) | ||
1795 | 最小二乗法 (Gauss) | ||
1865 | Clausius’ entropy (Clausius) | ||
1870s | Boltzmann’s entropy (Boltzmann) | ||
1901 | PCA (Pearson) | ||
1905 | ブラウン運動 (Einstein) | ||
1905 | ランダムウォーク (Pearson) | ||
1912-22 | 最尤推定 (Fisher) | ||
1920 | Ising model (Lenz) | ||
1943 | Neuron model (McCulloch and Pitts) | ||
1948 | Shannon’s entropy (Shannon) | ||
1949 | Hebbian Learning (Hebb) | ||
1952 | Hodgkin Huxley Eqation (Hodgkin and Huxley) | ||
1957 | Bellman equation (Bellman) | パーセプトロン (Rosenblatt) | |
1959 | V1 (Hubel and Wiesel) | ||
1960 | Delta rule (Widrow and Hoff) | ||
1966 | Hidden Markov Model (Baum and Petrie) | ||
1967 | k-means (MacQueen) | ||
1969 | Perceptrons (Minski and Papert) | ||
1973 | 自己組織化マップ (von der Malsberg) | ||
1977 | EMアルゴリズム (Dempster et al.) | ||
1980 | Neocognitron (福島) | ||
1980 | 自己組織化マップ (甘利) | ||
1982 | Hopfield Network (Hopfield) | Vision (Marr) | |
1983 | 自己組織化マップ (Kohonen) | ||
1986 | Back Propagation (Rumelhart et al.) | ||
Boltzmann Machine (Ackley, Hinton, Seinowski) | |||
1988 | Autoencoder (Baldi and Hornik) | ||
Autoencoder (Bourland and Lecun) | |||
1989 | LeNet (LeCun) | ||
1994 | ICA (Pierre) | ||
1995 | Positive Matrix Factorization (Paatero and Tapper) | ||
SVM (Vapnik) | |||
1997 | Sparse Coding (Olshausen et al.) | ||
1998 | Kernel PCA (Scholkopf et al.) | ||
1999 | Non-Negative Factorization (Lee and Seung) | ||
2001 | Topographic ICA (Hyvarinen et al.) | ||
2006 | IVA (Kim et al.) | Deep Belief Network (Hinton and Salakhutdinov) | |
Monte Calro Tree Search (Coulom) | |||
2007 | Stacked Autoencoder (Bengio et al.) | ||
2010 | Rectufied linear (Nair et al.) | ||
2012 | AlexNet (Krizhevsky, Sutskever, Hinton) | ||
VGG | |||
2015 | ResNet (He et al.) | ||
Deep Q network (Mnih et al.) | |||
2016 | Xception (Chollet) | ||
2017 | Squeeze-and-Excitation Networks (Hu et al.) | ||
AlphaGo Zero (Silver et al.) | |||
2018 | AlphaZero (Silver et al.) | ||
R2D2 |
Last Update: 2021/01/14
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