View allAll Photos Tagged NeuralNetworks
Here are some albums that you may appreciate:
Art - Main www.flickr.com/photos/jezevec/albums/72157605080379416
Art - Flower www.flickr.com/photos/jezevec/albums/72157697124422380/
AI - Main www.flickr.com/photos/jezevec/albums/72177720304402098/
AI - Flowers www.flickr.com/photos/jezevec/albums/72177720313064108/
Photos - Flowers www.flickr.com/photos/jezevec/albums/72157718673969071/
Taipei Rose Festival 2023 www.flickr.com/photos/jezevec/albums/72177720306812199/
Taipei Rose Festival 2021 www.flickr.com/photos/jezevec/albums/72157718673969051/
#FLOWERS4U #flower #floralfriday #flowerpower #flowerpics #flowerphotography #blom #lule #ծաղիկ #gül #кветка #ফুল #цвете #flor #花 #cvijet #květina #blomst #bloem #lill #bulaklak #kukka #fleur #ყვავილების #Blume #λουλούδι #ફૂલ #flè #फूल #virág #blóm #bunga #bláth #fiore #花 #ಹೂವು #ផ្កា #꽃 #ດອກ #florem #zieds #gėlė #цвет #fjura #blomst #kwiat #floare #цветок #kvetina #cvet #maua #blomma #மலர் #పువ్వు #ดอกไม้ #çiçek #квітка #hoa #blodau #wâbigin #nature #aard #natyrë #բնություն #təbiət #natura #প্রকৃতি #priroda #природа #naturalesa #kinaiyahan #chikhalidwe #大自然 #příroda #natur #natuur #loodus #kalikasan #luonto #natureza #ბუნება #natur #φύση #પ્રકૃતિ #yanayi #प्रकृति #természet #eðli #alam #nádúr #natura #自然 #ಪ್ರಕೃತಿ #табиғат #ធម្មជាតិ #자연 #ລັກສະນະ #daba #pobūdis #природата #പ്രകൃതി #निसर्ग #သဘာဝ #प्रकृति #natură #природа #narava #naturaleza #табиат #இயல்பு #ప్రకృతి #ธรรมชาติ #doğa #tabiat #натура #flowerstagram #flowerlovers #bloomsofinstagram #florallove #flowerpower #flowerbeauty #flowerphotography #petalperfection #floraladdict #flowergarden #bloomingbeauty #flowermagic #floral_perfection #flowersmakemehappy #floweraddiction #flowerfields #floralobsession #floralwonder #flowerlife #floraldesign #floweroftheday #flowergram #flowerlove #floralfix #flowerheaven #flowerart #floralinspiration #flower_daily #floraldecor #flowertherapy #AI_MAIN #ArtificialIntelligence #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #BigData#Tech #Innovation #AIArtistry #ArtificialArt #MachineArt #AIgenerativeart #ArtificialVision #NeuralArt #Cyberart #SyntheticDesign #DigitalPainting #AlgorithmicPainting #GeneratedArt #RoboticDesign #AIinspiredart #ArtificialInspiration #DataVisualization #TechDesign #ArtificialExpression #ArtificialBeauty #ArtificialBrilliance #TheArtOfMachine #AIInnovationInArt #AiArt #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #ArtificialCreativity #DigitalArt #GenerativeArt #TechArt #CyberpunkArt #FuturisticArt #RoboticArt #AIgenerated #SyntheticArt #CodeArt #DataArt #AlgorithmicArt #ArtificialImagination #TheArtofAI #AIandArt #AIinArt #ArteDeInteligenciaArtificial #ArteDeAprendizajeAutomatico #ArteDeAprendizajeProfundo #ArteDeRedesNeuronales #ArteDeCreatividadArtificial #ArteDigital #ArteGenerativo #ArteTecnológico #ArteCyberpunk #ArteFuturista #ArteRobótico #ArteGeneradoPorIA #ArteSintético #ArteDeCódigo #ArteDeDatos #ArteAlgorítmico #ImaginaciónArtificial #ElArteDeLaIA #InnovaciónEnElArteDeIA #ArteEInteligenciaArtificial #ArteInnovadorDeIA #ArtificialIntelligenceArt #ArtIntelligenceArtificielle #MachineArt #ArtMachine #DeepLearningArt #ArtApprentissageProfond #NeuralNetworksArt #ArtReseauxNeurones #ArtCréativitéArtificielle #CréativitéNumérique #ArtGénératif #ArtTech #ArtCyberpunk #ArtFuturiste #ArtRobotique #ArtGénéréparIA #DesignSynthétique #ArtCode #ArtDeDonnées #ArtAlgorithmique #ImaginationArtificielle #ArtEtIA #KünstlicheIntelligenzKunst #KIkunst #MaschinellesLernenKunst #MLKunst #TiefesLernenKunst #DLKunst #NeuronaleNetzeKunst #NNKunst #KünstlicheKreativität #Digitalekunst #GenerativeKunst #TechKunst #CyberpunkKunst #Zukunftskunst #RoboterKunst #IAgenerierteKunst #SynthetischeKunst #CodeKunst #DatenKunst #AlgorithmenKunst #KünstlicheImagination #KunstUndKI #人工知能アート #機械学習アート #深層学習アート #ニューラルネットワークアート #人工クリエイティブアート #デジタルアート #ジェネレーティブアート #テクノロジーアート #サイバーパンクアート #未来アート #ロボットアート #人工知能生成アート #シンセティックアート #コードアート #データアート #アルゴリズムアート #人工想像アート #人工知能とアート #人工知能革新アート #ArteDeInteligenciaArtificial #ArteDeAprendizajeAutomatico #ArteDeAprendizajeProfundo #ArteDeRedesNeuronales #ArteDeCreatividadArtificial #ArteDigital #ArteGenerativo #ArteTecnológico #ArteCyberpunk #ArteFuturista #ArteRobótico #ArteGeneradoPorIA #ArteSintético #ArteDeCódigo #ArteDeDatos #ArteAlgorítmico #ImaginaciónArtificial #ElArteDeLaIA #InnovaciónEnElArteDeIA #ArteEInteligenciaArtificial #ArteInnovadorDeIA
A recent ponderance of mine (about a year old) has been that genes store fractal memories, both relating to evolution re all the steps back to our genetic ancestors origins and also re experiences. It seems to me that fractals are a type of language and everything, material and immaterial can be represented by them in 'form'. I think that it is these fractal memories stored in the genes that direct evolution. I think it could also explain things of a pseudoscientific nature such as alleged 'past life' memories and transplant memories etc.
Meanwhile re 'evidence of fractal memory' in animals
Bees create hives which are mathematically correct structures and 'fractals'. I am suggesting that the knowledge of the mathematical requirements of building the hive is stored in their genes as 'fractal memory'. If not 'genetic memory' then how does this knowledge pass from parent to offspring?
Most animals have innate knowledge...how does 'innate' work if not via the genes? Innate knowledge must surely be stored as memory in the genes? My uneducated explanation (and please excuse if me if this is old news and I'm stating the obvious) is that evolution starts with the simplest fractal and as things became more complex, so did fractals. I beleive all living things have stored within their genes the genetic fractal memory of every earlier fractal development that their species went through. I believe all things can in the end be presented in fractals. It's a language if you like. Everything in this universe, material and not can be represented in form by a fractal. Thus fractal memory is 'everything' memory. Events etc. Memory of our genetic ancestors. I think that the fractal gene memory that I speak of here is what prompts evolutionary change. Ie. The genes are aware of the events of the hosts life and make changes accordingly for the benefit of the offspring. Note the 'awareness' is not conscious awareness as we understand it but the same type of 'automatic' functioning we see in robots which make 'corrections' to their behaviour when they bump into things.
We 'humans' have problems seeing events in terms of maths or 'fractals' because of our 'perception' but in reality it is just 'maths', so not all that bizarre that the gene which is a fine tuned computer can make adjustments in it's 'output' based on the 'input' . Think of this entire world as a digital programme and it all makes sense. Evolution is a software update and nothing more than that.Memory of our genetic ancestors. We discuss a model of memory for visual form which treats the memory 'trace' as a set of procedures for reconstructing earlier visual experience. The
procedures, Barnsley's Iterated Function System (IFS), construct an image from a collection of operators (affine transformations). From this perspective, remembering and imagining are processes whose dynamics are captured by
the iterative rules. Changes in memory for visual experience are described as changes in the parameters and weights of the reconstruction operators. The model is used to discuss known phenomena and effects in the empirical literature on memory for visual form. Fractals, as all present know, are irregular geometric objects that yield detail at all scales. Unlike Euclidean, differentiable, objects that smooth out when zoomed into, fractals continue to reveal features as more closely regarded. Fractals also have "self-similarity"
which, in one meaning refers to the presence of parts that resemble the whole, or to the continual repetition of a feature. Benoit Mandelbrot (1983) not only invented the term fractal, but advanced the position that fractal geometry is the geometry of nature. Eliot Porter, the nature photographer,
upon reading Gleick's (1988) account of Mandelbrot's work, realized he had been taking pictures of fractals in nature for decades. To promote the point, he collaborated with Gleick on a collection of photographs for a book titled Nature's Chaos (Porter & Gleick, 1990). The slides shown here are
samples from that book. From a Darwinian perspective, we propose that our sensory receptors evolved in the presence of fractal objects, bathed in and powerfully shaped by them. It makes sense to us, then, that fractal geometry should be adopted in the study of perception and memory for
visual form (cf. Gilden, Schmuckler, & Clayton, 1993). Yet contemporary psychophysical studies of perception are dominated by Euclidean measures, and modern theories of visual form, such as Biederman's (1987) object recognition theory, have Euclidean objects (spheres, cubes, etc.) as
primitives ("geons"). A general model. We first present a general, abstract, model of memory for visual form from this perspective, and then consider a more specific version (see figure 1). We assume, first, that memory is not a passive list of attributes, but rather is a set of mental procedures. These
procedures, when activated, allow a reconstruction of a semblance of the original experience. There are two classes of memory dynamics; those involved in remembering and imagining, and those involved in forgetting. These are very different procedures. The first is initiated by some query of
memory, arising internally or externally. It is relatively fast and interacts with ongoing cognitive demands and simultaneous visual experience. The second process, altering memory, is the result of subsequent experience. It acts to alter procedure parameters.
It has been demonstrated that higher order recurrent neural networks exhibit an underlying fractal attractor as an artifact of their dynamics. These fractal attractors offer a very efficient mechanism to encode visual memories in a neural substrate, since even a simple twelve weight network can encode a very large set of different images. The main problem in this memory model, which so far has remained unaddressed, is how to train the networks to learn these different attractors. Following other neural training methods this paper proposes a gradient descent method to learn the attractors. The method is based on an error function which examines the effects of the current network transform on the desired fractal attractor. It is tested across a bank of different target fractal attractors and at different noise levels. The results show positive performance across three error measures.
www.vanderbilt.edu/AnS/psychology/cogsci/clayton/papers/C...