#DeepNeuralNetworks

[totoshappylife]

Neural Constraints on Cognitive Experience and

Excerpt from PDF: Neural Constraints on Cognitive Experience and Mental Health Bita Shariatpanahi1†, Erfan Nozari2,3,4,5†, Soroush Daftarian1, Fahimeh Arab3, Mina Kheirkhah6, Felix P. Bernhard1, Shiva Khodadadi1, Erik J. Giltay7,8, Kaat Hebbrecht 7, Stefan G. Hofmann9, Tim Hahn10, Hamidreza Jamalabadi1,11* 1Department of Psychiatry and Psychotherapy, University of Marburg, Germany. 2Department of…

[physicsalertofficial]

Bayreuth mathematicians use AI to study galaxies What do galaxies look like? How do they behave in the long term? These are some of the questions that Dr. Sebastian Wolfschmidt and Christopher Straub, researchers at the University of Bayreuth, are trying to answer. They use mathematical models of galaxies that incorporate Einstein’s general theory of relativity, which explains how gravity affects space and time. However, these models are not easy to test, as astronomical observations are limited and numerical simulations are time-consuming. That’s why Wolfschmidt and Straub have developed a novel approach that uses artificial intelligence (AI) to quickly predict the stability of galaxy models. A deep […]

[gis56]

🚗💡 The Future of Autonomous Vehicles: Sensors Market Set to Explode by 2034!

Advanced Autonomous Vehicle Sensors Market : The evolution of autonomous vehicles (AVs) is powered by cutting-edge sensor technologies that enable real-time perception, navigation, and decision-making. Advanced LiDAR, radar, cameras, ultrasonic sensors, and AI-driven perception systems are revolutionizing the self-driving industry by enhancing object detection, depth mapping, and situational awareness. LiDAR sensors provide high-resolution 3D imaging for precise distance measurements, while radar technology ensures robust performance in adverse weather conditions. AI-powered vision systems process massive amounts of data to identify obstacles, road signs, and pedestrians, optimizing real-time decision-making for safer and more efficient transportation.

To Request Sample Report : https://www.globalinsightservices.com/request-sample/?id=GIS10009 &utm_source=SnehaPatil&utm_medium=Article

The autonomous vehicle sensor market is accelerating with innovations in solid-state LiDAR, edge computing, sensor fusion, and V2X (Vehicle-to-Everything) communication. These advancements improve AVs’ ability to react dynamically to complex driving environments while reducing costs and energy consumption. Companies like Tesla, Waymo, NVIDIA, and Mobileye are leading this transformation with cutting-edge AI integration and next-gen perception systems. As self-driving technology advances, sensor miniaturization, 5G connectivity, and deep learning algorithms will further refine the accuracy and reliability of autonomous navigation, driving us closer to a future of fully autonomous transportation. 🚀

#autonomousvehicles #lidar #radar #selfdrivingcars #adas #avtechnology #futureofmobility #smarttransportation #sensorfusion #connectedcars #aiintransportation #machinelearning #automotivesensors #deepneuralnetworks #selfdrivingtech #autonomoustech #mobilityinnovation #vehicletovehicle #v2xcommunication #automateddriving #intelligentmobility #aiinmobility #saferoads #driverlesscars #robotaxis #avindustry #perceptionai #computervision #automotiveinnovation #smartcities #nextgenmobility #avmarket #selfdrivingfuture #transportationtech #edgeai #autonomousdriving

[whats-ai]

What has AI Brought to Computer Vision? We are still far from mimicking our vision system even with the current depth of our networks, but is that really the goal of our algorithms? Would it be better to use them as a tool to improve our weaknesses? What are these weaknesses, and their strengths? Read the article: (link in story) https://ift.tt/3tSVGoZ posted on Instagram - https://instagr.am/p/CONcvY5A3js/

[generatedart]

Art by @unolab.uno Neural Style Transfer in machine learning 🧠 #generative #aiart #neuralstyle #neuralstyletransfer #nst #newmediaart #blackandwhite #hands #algorithmicart #algorithmicdesign #creativecoding #creativecodeart #creativecode #deepneuralnetworks #graphicjuice #acidgraphix #motiongraphics #abstractart #surrealart #opticalart #surrealism #digitalart #xuxoe #machinelearning #aiartcomm #aiartcommunity #graphicindex #digitalillustration #stylegan #deepdreamart https://www.instagram.com/p/CcK5M9sNgzR/?igshid=NGJjMDIxMWI=

[griffynrobotech]

Vision Inspection with AI and Deep learning is poised to make a major impact on manufacturing industry. To know more about the technology, read the blog, Harnessing AI and Deep Learning for Vision Inspection https://lnkd.in/d4n8dpC

[coerente-ai-blog]

Multiview disease analysis. Trying to recognize or predict a thing with a single perspective may have some flaws and won't be a generalized idea but if you try to interpret information from different resources, that could help you to come up with a generalized solution. Example: Predicting a seizure just by analyzing the EEG data may have some flaws but if you try to interpret information not only from EEG but also from mRi, pet. It could bring you generalized idea. Since, In medical field the accuracy of model is not so important but it's specifity does matter #nomizocoders #autoencoder #ai #artificialintelligence #python #golang #deepneuralnetworks #deeplearning #machinelearning #AI #jupyternotebook #sklearn #tensorflow #keras #pytorch #caffee #weights #biases #approximate #developer #programming #tamilprogrammer #java #javadeveloper #pythondeveloper #technology #science #computer #computerscience #engineering https://www.instagram.com/p/B_jgJmDA3Oa/?igshid=uw0a430f6hxr

[incegna]

Unsupervised Deep Learning in Python.Understand the theory behind autoencoders.Write a stacked denoising autoencoder in Theano and Tensorflow. Interested people can share me your details. Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com #Unsupervised,#deeplearning,#autoencoders,#Theano,#tensorflow,#Boltzmannmachines,#deepneuralnetworks,#artificialneuralnetworks,#tsne,#PCA,#scikit,#pythonmachinelearning,#bayies,#Adversarialnetworks,#GAN,#numpy https://www.instagram.com/p/B-HJVaVgHKU/?igshid=hnb2i2uzngm7

[clunite]

Using Deep Learning to detect a face. It gives much better accuracy as compare to OpenCV's Haar Cascades. ⠀ ⠀ Also, at the same time (in the background) building my custom dataset of images to further perform facial recognition and collect training data.⠀ ⠀ Thanks for Adrian @PyImageSearch for his amazing tutorials. ⠀ ⠀ ⠀ ⠀ #WeekEndGrind #WeekEnd #WeekEndProject #DeepLearning #AI #ArtificalIntelligence #MachineLearning #NerualNetwork #NN #CaffeNet #OpenCV #FaceDetection #FaceRecognition #DeepNeuralNetwork #DNN #ConvolutionalNeuralNetwork #CNN #CaffeNet #OpenCV2 #ComputerVision (at Brampton, Ontario) https://www.instagram.com/p/B5SaITAHJT5/?igshid=7s79ggft6z0u

[miniaturekittenkitty]

What is deep learning, Meaning And work? || LN-TECHINFO

Deep learning is a sub-field of machine learning and an aspect of artificial intelligence. To understand this more easily, understand that it is meant to emulate the learning approach that humans use to acquire certain types of knowledge.

This is somewhat different from machine learning, often people get confused in this and machine learning. Deep learning uses a sequencing algorithm while machine learning uses a linear algorithm.

To understand this more accurately, understand this example that if a child is identified with a flower, then he will ask again and again, is this flower? For him, every colorful thing will be a flower, he will slowly index things according to the flowers and slowly he will know the flower. It develops over time.

How does deep learning work?

Deep learning each algorithm applies a non-linear transformation to its input and converts it into a statistical model from what it learns from the input. And it continues its effort until the exact output is found.

Whereas in traditional machine learning, the learning process is monitored and the programmer should be very specific when telling the programmer what kinds of things to search during decision making.

This is a laborious process called feature extraction and the success rate of a computer depends entirely on the ability of the programmer to define a feature.

The advantage of deep learning is that the program creates a self-determined facility without supervision. Not only is untrained education faster, but it is also generally more accurate.

For example, suppose you make the computer familiar with the shape of a flower, but it makes the pattern not from its petals or designs, but from the pixel, with the help of which it knows the flower.

What is deep neural networking?

The way of thinking of deep learning is exactly like human neuron, so it is often called Deep Neural Learning and Deep Neural Networking.

It may take a few days for a small child to consider a flower as a flower, but deep neural networking can identify a picture of a flower in a few minutes out of millions of pictures.

To do this one has to achieve an acceptable level of accuracy for which deep learning programs need access to an enormous amount of training data and processing power. Earlier it was not so easy but in the century of cloud computing and large data base it is easily done.

Unstructured data can also be used quite easily through deep neural networking. However, most of the data collected is unstructured.

Deep learning usage

Deep learning is being used quite rapidly in today's time, almost every big company is using it, or wants to do it.

Some of its recent big usage has been done by the big phone companies, which include these things.

Image Recognition - This means recognizing a picture, it can often be seen easily in mobile phones.

Speech Recognition - Its job is to recognize the voice of the people.

Translator - Its function is to convert one language into another language. Many more examples of deep learning can also be seen.

Deep learning limits

The biggest limitation of deep learning is that it learns only through observation. This means that the data given to it knows only that much.

If no one has a large amount of data available then it will not work in that condition. If the data is collected in a biased manner, then the result obtained will also be more inclined towards any one. That is, whatever you give it, it will learn from it and will give you results.

Contact us : https://lntechinfo.in/contacts/

[pantechlearning]

Neural network is the present and the future. The different types of neural networks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and others have made huge improvements in the Deep Learning technology.

Neural Networks are in fact advance machine learning algorithms. The key to the efficiency of neural networks is they are very much adaptive and they can learn very quickly.

What are Neural Networks?

Neural networks are also known as Artificial Neural Networks (ANNs). They are a subset of machine learning and are at the heart of deep learning algorithms. The human brain inspires the development of neural network. They work by mimicking the way that biological neurons signal to one another.

Neural networks depend on their training data. Thus they can learn and improve their accuracy over time. Speech recognition or image recognition using Neural Networks can take only minutes versus hours when comparing with the manual identification by human experts. Google’s search algorithm is one of the famous neural networks.

How do they work?

There are many processors in Neural Network. These processors arrange as tiers. The first tier receives the unprocessed input similar to how the human optic nerve receives the raw information.

Every successive tier then accepts input from the tier before it. Then they pass its output to the tier after it. The last tier provides the final output.

Each tier has small nodes. The nodes are connecting to the nodes in the tier before and after. Also, each node in the neural network has its own type of knowledge. This including its programming rules and also the rules it has learnt by itself.

Why do we need to Learn them?

Neural networks are the main part of most of the deep learning applications. Therefore, from facial recognition and object detection, to building human-like chatbots, neural networks are essential.

Neural networking technology is the present and the future of Science. So you should start learning right now!

Pantech eLearning is an Online Learning Service provider in Chennai. We are offering a course in Neural Network. This course will help you to learn and understand the Deep Learning and Neural Networking technology deeply. The course is totally free of cost. You will get a lifetime access. The total course duration is 1 week. Also, we offer online guidance and teaching with top experts to support you.

Visit our Website and Book your Courses Now.

Applications of Neural Network

Since neural networks offer various problem solving techniques, there is virtually no limit to the areas for its application.

Some common applications of neural networks today are image or pattern recognition, self-driving vehicles, facial recognition, data mining, email spam filtering, medical diagnosis, and cancer research.

There are many other ways that neural nets are using today, and the usage is increasing rapidly.

[source-soft]

Google’s BigBird model enables Transformer neural networks to process 8x longer input sequences. The researchers at Google used this model to build an application for Transformer networks in genomic sequence representations.

[aihindishow-blog]

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A post shared by Sameer Nigam| Machine Learning Engineer | AI Mentor (@aiwithsameer)

EYEBALL MOVEMENT TRACKING 👀👀 . . . Sameer Nigam is a Machine learning engineer having 1 year of experience in development using python, Django, machine learning and computer vision. He also writes articles on artificial intelligence and machine learning and currently is a host of a podcast named AI Hindi Show. . . . Our podcasts are designed to provide value to you. You can expect a different kind of topic every day to discuss but related to artificial intelligence, machine learning, and python programming in Hindi. Hence creating us the first all in one podcast in India related to AI, ML, and Python. You can even mail us or dm us your suggestions for any topic to air on the podcast. We will also mention your name. . . . . All useful links in BIO. . . . . . #facerecognition #artificialintelligence #machinelearning #deeplearning #ai #ml #neuralnetworks #fasterrcnn #convolutionalneuralnetwork #deepneuralnetworks #hindipodcast #podcast #indianpodcast #cnn #machinelearningalgorithms #opencv #openvino #keras #dataset #datascience #machinelearningengineer #myskillsaremysuperpowers #practiceexperimentimpliment #aihindishow #sameernigam #podcastlife #computervision #computervisiontechnology (at Lucknow, Uttar Pradesh) https://www.instagram.com/p/B6VA5bXn14O/?igshid=hpjoxvbukpwe

[fracto]

GHOST IN THE SHELL OST Ghost in the Shell Original Soundtrack Publicado por: Japan Galaxy {Captura de pantalla del cover del álbum de la banda sonora del filme Ghost in the Shell, tomada desde el canal oficial Japan Galaxy en YouTube} #GhostintheShell #cyberpunk #machines #ai #ia #artificialintelligence #inteligenciaartificial #algoritms #algoritmos #machinelearning #deeplearning #aprendizajedelasmáquinas #aprendizajeprofundo #redesneuronalesprofundas #deepneuralnetworks #enfoquesdiferenciales #differentialapproaches #transhumanity #transhumanidad #transculturality #transculturalidad #cibernetics #cibernéticas

[coerente-ai-blog]

The subsets of machine learning.

#nomizocoders #machinelearning #deeplearning #cnn #AI #artificialintelligence #deepneuralnetworks #nlp #imageclassification #vgg16 #cancer #coerenteai #followforfollowback #follow4followback #python #golang #subsets #gpt2 #logisticregression #naivebayes #neuralnetworks #randomforests #autoencoder #encoderdecoder

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