Category:Deep learning
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Unterkategorien
Es werden 4 von insgesamt 4 Unterkategorien in dieser Kategorie angezeigt:
In Klammern die Anzahl der enthaltenen Kategorien (K), Seiten (S), Dateien (D)
Medien in der Kategorie „Deep learning“
Folgende 94 Dateien sind in dieser Kategorie, von 94 insgesamt.
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De-Deep Learning.ogg 2,0 s; 19 KB
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1951-test.wav 50 s; 3,08 MB
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2020 lernwerkstatt deep learning.jpg 2.315 × 2.249; 2,82 MB
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A development of natural language processing tools.png 1.414 × 2.000; 108 KB
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Activation gelu.png 1.200 × 800; 65 KB
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ActivationFunctions.svg 1.058 × 606; 293 KB
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AI hierarchy pl.svg 399 × 399; 4 KB
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AI hierarchy.svg 399 × 399; 8 KB
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AI Types. Tipos Inteligencia Artificial.svg 953 × 899; 22 KB
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AI-ML-DL-ar.png 859 × 972; 89 KB
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AI-ML-DL-bn.svg 701 × 775; 8 KB
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AI-ML-DL.png 859 × 972; 32 KB
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AI-ML-DL.svg 701 × 775; 18 KB
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AmrullahDeepLiquidityBTCUSD.png 1.373 × 703; 105 KB
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An AI learns basic physical principles.webp 1.033 × 472; 45 KB
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Artificial Neural Network with Chip.jpg 2.000 × 1.600; 2,59 MB
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ATW CNN architecture.png 3.360 × 5.019; 807 KB
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Causual language modeling.jpg 2.000 × 1.414; 68 KB
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Chainer.001.jpg 1.024 × 768; 169 KB
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Comparison of speed and accuracy of detectors.png 1.094 × 1.021; 363 KB
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ConvolutionAndPooling.svg 839 × 208; 160 KB
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Dated lunar crater size-frequency distributions.webp 2.000 × 2.248; 685 KB
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Dcgan(128128 63k).gif 720 × 720; 34,06 MB
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Dcgan(128128 9k).gif 720 × 720; 92,91 MB
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Dcgan(6464 63k).gif 720 × 720; 74,07 MB
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Dcgan2(6464 9k).gif 720 × 720; 70,65 MB
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Deep Learning Indaba - SisonkeBiotik Community Keynote.pdf 2.000 × 1.125, 77 Seiten; 6,64 MB
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Deep Learning.jpg 1.239 × 1.012; 341 KB
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Deep Thinkers on Deep Learning.jpg 2.044 × 1.277; 672 KB
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DeepInsight method to transform non-image data to 2D image for convolutional neural network architecture.pdf 1.239 × 1.629, 7 Seiten; 1,78 MB
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DeepInsight Pipeline.jpg 1.200 × 425; 143 KB
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DeepInsight RevealingPatterns.jpg 3.809 × 3.312; 1,75 MB
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DeepLearningReconstruction.png 2.502 × 600; 617 KB
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Distribution of lunar impact craters on the Moon.webp 1.999 × 1.543; 215 KB
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Double descent in a two-layer neural network (Figure 3a from Rocks et al. 2022).png 3.302 × 1.530; 396 KB
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EncoderDecoder.pdf 1.275 × 458; 9 KB
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Feature Learning Diagram.png 1.000 × 333; 32 KB
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Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models.pdf 1.275 × 1.650, 9 Seiten; 1,25 MB
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Generative Adversarial Network illustration.svg 1.021 × 378; 4 KB
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Graph attention network.png 1.228 × 801; 145 KB
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Higher order message passing.png 1.004 × 324; 73 KB
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Higher order networks.png 1.241 × 355; 107 KB
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Illustration of 3D-GCN's receptive field and kernel.png 948 × 552; 275 KB
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ImageNet error rate history (just systems).svg 810 × 990; 34 KB
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Infinitely wide neural network.webm 8,0 s, 1.920 × 1.080; 9,41 MB
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Keras model inner32 epochs500.svg 575 × 431; 934 KB
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Knowledge Distillation Flow Chart.jpg 996 × 630; 103 KB
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Knowledge-distillation-cv.png 464 × 287; 114 KB
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Knowledge-distillation-example.png 714 × 332; 82 KB
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Learning tasks on topological spaces.jpg 891 × 698; 73 KB
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LeNet 5 Architecture.png 1.022 × 772; 42 KB
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LSTM cell.svg 524 × 341; 26 KB
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Machine Learning.pdf 1.250 × 1.766, 22 Seiten; 6,63 MB
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MachineLearningPanelDiscussionAtIEEETechIgnite2017.jpg 4.128 × 2.322; 2,73 MB
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Masked language modelling.jpg 2.000 × 1.414; 75 KB
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Multiple attention heads.png 870 × 1.280; 269 KB
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NeedForDeeperLayers.svg 834 × 547; 692 KB
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Neural Abstraction Pyramid.jpg 1.343 × 782; 253 KB
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Object detector 1stage vs 2 stage.png 1.085 × 867; 107 KB
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One-neuron recurrent network bifurcation diagram.png 800 × 800; 209 KB
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Penny serenade colorized moonflix 3.0.jpg 2.870 × 2.095; 389 KB
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Percepto Autonomous Drone Landing in Strong Winds.webm 46 s, 198 × 360; 6,99 MB
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Perceptualapplications.png 2.610 × 666; 1,73 MB
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Pngbase64e71e96131752d94.png 1.037 × 610; 18 KB
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Positional encoding.png 1.600 × 800; 217 KB
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Random Multimodel Deep Learning (RMDL).png 16.333 × 11.200; 13,13 MB
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Real-time-classification-and-sensor-fusion-with-a-spiking-deep-belief-network-Movie1.ogv 0,0 s, 1.200 × 736; 12,13 MB
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Reparameterization Trick uk.jpg 1.106 × 784; 117 KB
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Reparameterization Trick.jpg 1.106 × 784; 105 KB
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Reparameterization Trick.png 1.104 × 784; 69 KB
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Reparameterized Variational Autoencoder uk.jpg 1.264 × 566; 64 KB
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Reparameterized Variational Autoencoder.jpg 1.264 × 566; 69 KB
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Reparameterized Variational Autoencoder.png 1.264 × 524; 28 KB
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RLHF diagram.svg 512 × 366; 160 KB
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Russ Salakhutdinov.jpg 410 × 493; 53 KB
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Scene graph example.png 836 × 773; 422 KB
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Step-2 Live twinning.pdf 3.000 × 1.687; 327 KB
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Studierfenster.png 3.000 × 489; 62 KB
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StyleGAN-1 and StyleGAN-2.png 5.400 × 2.335; 496 KB
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Summary of CNN architectures.png 1.020 × 774; 85 KB
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Sweet Dreams by David S. Soriano.png 1.152 × 1.152; 3,43 MB
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The LSTM Cell.svg 673 × 461; 55 KB
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The-Transformer-model-architecture.png 850 × 765; 47 KB
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VAE Basic uk.jpg 1.122 × 486; 64 KB
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VAE Basic.jpg 1.122 × 486; 61 KB
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VAE Basic.png 1.122 × 484; 26 KB
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Yann LeCun at the University of Minnesota.jpg 3.264 × 2.448; 586 KB
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Yoshua Bengio, October 27, 2016.jpg 316 × 384; 53 KB