Neural Network History - Search Videos

A brief history of neural networks, excerpt from my video "I Built an LLM From Scratch"

A brief history of neural networks, excerpt from my video "I Built an LLM From Scratch"

2.6K views2 months ago

x.comCJ (Coding Garden)

AI • Machine Learning • Tech on Instagram: "Backpropagation, the algorithm that efficiently computes gradients for training neural networks, went through several key stages in AI history. Early neural network research in the 1960s, including work by Marvin Minsky and Seymour Papert, highlighted serious limitations of simple perceptrons, which contributed to reduced interest in neural networks for a time. In the 1970s, Paul Werbos proposed using the chain rule to propagate error gradients backwar

AI • Machine Learning • Tech on Instagram: "Backpropagation, the algorithm that efficiently computes gradients for training neural networks, went through several key stages in AI history. Early neural network research in the 1960s, including work by Marvin Minsky and Seymour Papert, highlighted serious limitations of simple perceptrons, which contributed to reduced interest in neural networks for a time. In the 1970s, Paul Werbos proposed using the chain rule to propagate error gradients backwar

71.6K views8 months ago

Instagramaibutsimple

The biggest open AI neural network dataset in history just dropped.Neuraxon 2.0 | 1.1TB of tick-by-tick neural dynamics from Qubic’s Aigarth system. Now live on @huggingface.The numbers: → 190× bigger than Neuraxon 1M → 1,200 neurons per game (was 120) → 375.5M synapses (was 15.7M — 24× growth) → Full time-series. Not snapshots. → 4TB more being processed nowThis is open science at a scale, no one else is doing.

The biggest open AI neural network dataset in history just dropped.Neuraxon 2.0 | 1.1TB of tick-by-tick neural dynamics from Qubic’s Aigarth system. Now live on @huggingface.The numbers: → 190× bigger than Neuraxon 1M → 1,200 neurons per game (was 120) → 375.5M synapses (was 15.7M — 24× growth) → Full time-series. Not snapshots. → 4TB more being processed nowThis is open science at a scale, no one else is doing.

688 views7 months ago

x.comMr Lights💰

THIS IS WHAT A NEURAL NETWORK ACTUALLY LOOKS LIKE WHEN SOMEONE BUILDS IT AS ART.An interactive museum exhibit, layer by layer, watch a neural network physically construct itself, thousands of connection points folding out into the exact structure models like GLM-5.2 and GPT-5.5 run on under the hood.It's the same concept every "open-weight" or "trillion parameter" headline is actually describing, just made visible. Layers stacking, nodes connecting, intelligence built one weighted link at a time

THIS IS WHAT A NEURAL NETWORK ACTUALLY LOOKS LIKE WHEN SOMEONE BUILDS IT AS ART.An interactive museum exhibit, layer by layer, watch a neural network physically construct itself, thousands of connection points folding out into the exact structure models like GLM-5.2 and GPT-5.5 run on under the hood.It's the same concept every "open-weight" or "trillion parameter" headline is actually describing, just made visible. Layers stacking, nodes connecting, intelligence built one weighted link at a time

1.1K views2 months ago

Massimo (@Rainmaker1973) on X

Massimo (@Rainmaker1973) on X

137.9K views3 months ago

Convolutional Neural Network (CNN) in Deep Learning - GeeksforGeeks

Convolutional Neural Network (CNN) in Deep Learning - GeeksforGeeks

geeksforgeeks.org

AI models are built from billions of mathematical connections that work together like a digital neural network. These connections are organized into multiple layers, processing information and recognizing patterns before generating an output. During training, the model adjusts billions of parameters using massive amounts of data, gradually learning how to make better predictions. This visualization highlights just how complex these networks can become, with countless mathematical operations work

AI models are built from billions of mathematical connections that work together like a digital neural network. These connections are organized into multiple layers, processing information and recognizing patterns before generating an output. During training, the model adjusts billions of parameters using massive amounts of data, gradually learning how to make better predictions. This visualization highlights just how complex these networks can become, with countless mathematical operations work

187 views1 month ago

ImageNet classification with deep convolutional neural networks | Communications of the ACM

EIE: efficient inference engine on compressed deep neural network: ACM SIGARCH Computer Architecture News: Vol 44, No 3

Heterogeneous Graph Neural Network | Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

[DL] A Survey of FPGA-based Neural Network Inference Accelerators | ACM Transactions on Reconfigurable Technology and Systems

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction | Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Happiness as an adaptive signaling system

8.2K views2 weeks ago

Can my neural network learn to drive? 1500 cars, 40 generations #ai #machinelearning #evolution #neuralnetworks

175.9K views2 weeks ago

TikTokmaths_divine

What Is a Neural Network? Differentiable Computational Graph Explained

2.7K views1 month ago

TikToksabrina_ramonov

This man just built a living neural network inside Obsidian using Claude Code. A pulsing graph of 200+ neurons across layers: Prefrontal, Motor Cortex, Hippocampus. Real-time firing. Connected to every .md file in the vault. Voice command: "Hey Jarvis, explain what I'm wearing." It instantly described the maroon hoodie and black shirt underneath. Then it explained the system: persistent memory pulled from every .md file - notes, tasks, personality, history. No context loss. Full recall across se

5.3K views2 months ago

What Is a Neural Network? CNN, RNN & Transformer Explained

9.3K views3 months ago

TikTokmindfuelbyali

Your memories aren't static video files; they are a plastic neural network. Scientists have shown that when you recall a past experience, it temporarily becomes vulnerable, allowing the emotional response to be modified. How does science view this brain

333 views3 weeks ago

TikTokphysicsprodigy

What is Neural Network: Overview, Applications, and Advantages

simplilearn.com

Very cool demonstration how a convolutional neural network classifies hand-written digits - circa 1989 Yann LeCun was the first to apply backpropagation to gradient-based learning to train neural… | Wayne Dalton

linkedin.comWayne Dalton

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