Ramesh Vijayaraghavan 👋🏽’s Post

Amazing graphical representation of a neural net, never seen anything like it. ↓ Are you technical? Check out https://AlphaSignal.ai to get a daily summary of breakthrough models, repos and papers in AI. Read by 200,000+ devs.

I am obsessed with watching this! Neural Network in its most intuitive visual.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗿𝘂𝗹𝘆 𝗮𝗺𝗮𝘇𝗶𝗻𝗴! Read this bottom-up: each one of those tiny little cells capture a tiny fraction of the larger set of characterics that make up a single digit. When it eventually gets the "full picture" and at the top layer the prediction is fully stable on one cell only, that's because only the cells sensible to the characteristics of the depicted digit are "activated".

Great visualization of #ANN to use in classrooms. 

We live in a world overflowing with information, and this visualization offers a simple way to think about AI. During my career, I’ve noticed that the overuse of fancy jargon around new technologies can push people away. It often makes them feel less confident, just because of some buzzword being thrown around. Remember, there was a time when everything was on a single computer—both the UI and the database lived together, and it was all so straightforward. The next time you encounter a new, complicated-sounding term, try relating it back to that basic model. Understand what it really stands for, and you’ll thank me later. :)

Absolutely fascinated by this stunning graphical representation of neural activity! 🧠✨ Visualizing the complex workings of the brain in such a detailed manner is truly amazing. #Neuroscience #NeuralActivity #DataVisualization #Innovation

WoW This is awesome, no need to try and visualise brain's working anymore 😉 seriously, This makes understanding and visualising neural networks much easier. you can see how the nodes, layers, weighted sum function work together. Here’s a concise explanation of neural networks: Structure: Neural networks have three key layers: 1. Input Layer: Takes raw data like images or numbers. 2. Hidden Layers: Perform computations by processing inputs through connected neurons. 3. Output Layer: Provides the final prediction or result. Connections: Neurons in one layer are fully connected to neurons in the next via weights and biases. How It Works: 1. Forward Propagation: - Data flows from the input layer through the hidden layers to the output layer. - Each neuron calculates a weighted sum of its inputs and applies an activation function to decide whether to pass information forward. 2. Error Calculation: The network’s output is compared to actual values using a loss function, which measures error. 3. Backpropagation: - Errors are propagated backward to adjust weights and biases using optimization techniques like gradient descent, improving accuracy iteratively. Learning Process: Neural networks learn by repeating forward propagation and backpropagation over multiple epochs (iterations), reducing error with each step. Applications - Image and speech recognition - Natural language processing (e.g., chatbots) - Predictive analytics in finance and healthcare This interconnected system mimics how the brain processes information, enabling advanced problem-solving and pattern recognition.

For those of you who are interested in AI but don't have a great understanding of how a neural net works, this is an EXCELLENT visual representation of what's going on. It is important to remember that this is an abstract visual representation that does a great job of showing how the algorithms in a neural net work. It's not an actual physical description of a neural net although it does physically describe something kind of somewhat similar to the human brain. Anyway, this is a really cool and useful video.

Very nice visualization of a neural net.