Pixel-Art AI Arcade
Interconnected layers of artificial neurons that learn patterns from data, inspired by the biological architecture of the human brain.
layers = [input, hidden, output]
Self-attention mechanisms that process sequences in parallel, powering modern language models with remarkable contextual understanding.
attention(Q, K, V)
Agents learn optimal strategies through trial and error, maximizing cumulative reward in dynamic environments.
reward = env.step(action)
Create new data instances from learned distributions, from photorealistic images to coherent text and music.
sample = model.generate(z)
Teaching machines to interpret and understand visual information from the world through convolutional architectures.
features = conv2d(image)
Dense vector representations that capture semantic meaning, mapping words, images, and concepts into continuous geometric spaces.
vec = embed("concept")
Iteratively denoise random signals into structured outputs, learning the reverse process of gradual corruption.
x_t = denoise(x_t+1, t)
Process relational data by passing messages between connected nodes, capturing complex structural dependencies.
h = aggregate(neighbors)
Gradient-based methods that navigate loss landscapes, finding parameter configurations that minimize prediction errors.
w -= lr * grad(loss)