#Neural Network

Neural networks and machine learning.

Most people use AI every day, but almost nobody knows what the inside of a neural network looks like. This visualization changes that. What you’re seeing is a simplified model of how artificial neurons fire, pass signals, strengthen connections, and form patterns. The lines represent hundreds of tiny pathways lighting up as the network “learns” from data. Neural networks power almost everything today: ✔️ ChatGPT and Gemini ✔️ Image and video generation ✔️ Speech recognition ✔️ Self-driving cars ✔️ Robotics and automation It all starts with systems like this millions of small connections forming one big digital brain. ➡️ Comment “Newsletter” to join thousands of readers getting the best AI news, prompts, and tools for free #ai #artificialintelligence #neuralnetwork #machinelearning #tech

This is what a 3D visualization of a neural network looks like. Save & follow for more. . Dm for credit. . . #coding #programming #software #aritficialintelligence #ai #machinelearning #software #coder #softwarengineering #neuralnetwork #viral #animation #computer #cse #python #java #code

How do neural networks actually learn? They use weights and biases that are initially randomized but are tweaked during the training process. This involves the chain rule and partial derivatives of the loss function with respect to weights and biases. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #neuralnetwork

A neural network works like a small digital brain 🧠💻 that learns from data. It takes inputs, finds hidden patterns 🔍✨, and passes the information through layers where each layer learns something new 🔗➡️. All layers together decide the final output with high accuracy 🎯🤖. . . . #neuralnetwork #machinelearning #ai #deeplearning #tech #coding #datascience #programming #aitech #futuretech #developer #codehelping #techcontent #artificialintelligence #neuronmodel #mlalgorithm #aitrends #techcommunity #computervision #nndesign #deeplearningmodel #aitutorials #learnai #mlengineer #neuralnets #datascientist #techcreator

You’re looking at a real neural network. Not the machine learning kind. The biological kind. Everyone’s building bigger models. I’m building a brain. I built a neuromorphic AI platform with 1 million spiking neurons, 11 brain regions, and 1.2 billion connections. It doesn’t memorize training data. It learns continuously from real experience, the same way a biological brain does. The reason I started building this is pretty simple. Every time an AI model gets smarter, it costs more energy, more hardware, more money. A single query to a large language model uses more power than running this entire brain for an hour. That math doesn’t work long term, and I don’t think brute force compute is how intelligence actually works in nature. So I went the other direction. This system runs on a single CPU, uses less than 5 watts, and never stops learning. No retraining. No massive datasets. No data center. It forms its own concepts, builds associations between things it sees and hears, develops reflexes, and adapts to situations it’s never encountered before. All on its own. The architecture is modeled after real neuroscience. There’s a sensory cortex for vision, audio, and touch. An association cortex that binds those signals together. A predictive layer that anticipates what comes next and pays more attention when it’s wrong. Motor cortex for movement and speech. A brainstem that manages energy and survival. Every connection strengthens or weakens based on experience. Nothing is hardcoded. One thing I built in from the start is a safety kernel. Every motor command the brain generates passes through a safety supervisor before it can reach the real world. It checks joint limits, force thresholds, and collision boundaries. If something looks dangerous, the system triggers a reflex withdrawal before the action ever executes. The brain can learn freely, but it can’t act without clearance. That’s not a feature I added later. It’s part of the architecture. The brain is live right now and will disclose demos to serious individuals. I am looking for researchers that would like to join me in neuromorphic hardware/computing for this next shuttle. Patent pending

Ever wondered what a Neural Network looks like in 3D? 🧠✨ This isn’t just a visualization... it’s a deep dive into how Artificial Intelligence (AI) actually thinks. From the simple Perceptron (the first neural model) to complex architectures like Multilayer Perceptrons (MLP),*Convolutional Neural Networks (CNN), and Spiking Neural Networks (SNN) every layer, node, and connection comes alive in 3D. These simulations reveal the hidden world of Deep Learning, showing how data flows through neurons, how features are extracted, and how machines learn to see, decide, and create. 🌐 Whether you’re into machine learning, computer vision, or AI research, this 3D journey shows the evolution of how intelligence is built... one neuron at a time. Credits: Denis Dmitriev on YT 🚀 Follow @deeprag.ai for more stunning AI visuals, neural network breakdowns, and the science behind machine intelligence. . . . . #neuralnetworks #deeplearning #machinelearning #artificialintelligence #computervision #ml #aiart #aivisualization #neuralnetworkvisualization #datascience #deepragAI #aiupdate #technology #aiworld #aiinnovation #neuralnetwork3D #futureofai

Visualizing the architecture of intelligence. 🕸️✨ Every neural network is built on the same fundamental concept: Layers. 🟡 Input Layer: Receives the raw data (pixels, text, numbers). 🟢 Hidden Layers: Where the magic happens—processing features and finding patterns. 🟠 Output Layer: Delivers the final prediction or decision. From the simple Perceptron to the complex loops of an RNN, these structures are the blueprints for how machines learn. 📐 #NeuralNetworks #MachineLearning #DeepLearning #DataScience #AI #Education #Visualized

Ever wonder what’s actually happening inside the "black box" when you hit run? 🧠💻 ​On the left: The Neural Network architecture lighting up as weights are adjusted across hidden layers. On the right: The loss landscape. Watch how gradient descent navigates the complex, multi-dimensional terrain to minimize the loss function, effectively teaching the model to reduce its error rate to a final loss of 0.0083 🚨 Comment "Neural" to get free code repo! #quantfinance #algorithmictrading #math #neuralnetwork #machinelearning

Os LLMs nasceram das redes neurais profundas 🧠⚙️ Tudo começa com redes neurais artificiais, inspiradas no cérebro humano. À medida que essas redes ganharam mais camadas, surgiu o Deep Learning, capaz de aprender padrões cada vez mais complexos. Quando esse poder foi aplicado à linguagem, veio a revolução: arquiteturas como os Transformers trouxeram o mecanismo de atenção, permitindo que o modelo entendesse contexto, significado e relações entre palavras — não só uma por vez, mas tudo ao mesmo tempo. O resultado? Large Language Models com bilhões de parâmetros, treinados em volumes massivos de texto, capazes de compreender, gerar e raciocinar em linguagem natural. De neurônios artificiais ➝ redes profundas ➝ atenção ➝ LLMs. Isso não é mágica. É engenharia + matemática + escala. 🚀 #InteligenciaArtificial #DeepLearning #RedesNeurais #LLM #AI MachineLearning Transformers

FINDING THAT CONNECTION It’s been 2 years since I first shared this gem with you! You’re watching two neurons that I saw under the microscope sensing one another and connecting. There are 86 billion neurons in the brain - how do they know how to connect to other neurons or body parts when our bodies are developing? They use these webbed hand-like structures that you can see in this video. The finger like projections actively sense the environment around it. When we are developing in utero, you’ll find these “growth cones,” at the tip of every growing neuron, actively searching their way between cells, trying to find the right spot to connect to. When they make their connection, they become resorbed and disappear. I know - it’s heartbreaking that the video ends right when we get to the exciting part, but see the black wavering line in the bottom right? That’s what they look like after they’ve connected together in a Petri dish. When people see this video they often ask, is this what happens when we learn new things? Unfortunately not. Growth cones aren’t responsible for the connections between neurons that form in learning and memory (synapses). Those connections are much smaller and appear as thousands of tiny bumps along the length of the part of a neuron called a dendrite.‬ ‪ This is a video I took of a neuron in a culture dish that I was just about to toss out. I looked at it under the microscope and saw that something interesting was about to happen, so set up a recording. This video has been sped up - it’s the growth that occurred over the space of 20 minutes. This video belongs to me, Dr Lila Landowski. I am very happy for you to share it for teaching purposes, but please acknowledge me accordingly. I keep seeing my video pop up without attribution, so I’ve made this one with a watermark 🥺 #neuroscience #neurology #neurosurgery #brain #brains #neuron #neurons #research #cellculture #laboratory #science #scienceporn #learning #brainhealth #brainawarenessweek #neuro #neuroplasticity #brainteaser #lablife #education #teaching #teachingtips #biology #biologia #viralreels #trendingreels #reelsexplore #reelsoftheday #instagramreels

Neuronal connections form the intricate web of communication in our brains, enabling thoughts, emotions, and actions. Each synapse is a bridge, transmitting signals that shape our experiences. Follow us @bioscoolya 👉 For latest Biology news and research 👉 Learning new things daily 👉 Learn Biology in Fun way with us #Neuroscience #Brain #Neurons #NeuralNetwork #BrainHealth #Cognition #Neuroplasticity #Mindfulness #MentalHealth #Learning #Memory #Science #Psychology #BrainScience #Neurobiology #reels #instagram #reelsinstagram #aesthetic #animation #3d #igers #ig #biologystudents #neurobiology #bioscoolya