Visual AI Lab

A Discord-style server to understand artificial intelligence hands-on: every channel is a 3D scene guided by a tutor, driven by slash commands.

🧠 Neural networks

From a single neuron to a CNN: what makes a network learn, and what makes it derail.

#neuron — Let us break a neuron apart: inputs, weights, sum, activation.
#dropout — Dropout regularization: the best friend of deep networks.
#activation — Why an activation? ReLU, sigmoid, tanh, Leaky ReLU, GELU and the vanishing gradient.
#gradient-descent — Loss is a landscape. The gradient says which way is up, so we step the other way.
#backpropagation — The computation graph replayed backwards: each node receives ∂L/∂(itself) and the chain rule does the rest.
#overfitting — A big model on few data points: the decision boundary twists until it memorizes the noise.
#cnn-filters — An image is a grid of numbers. A 3×3 filter slides over it, multiplies, adds up: that is a convolution.
#is-it-einstein — Is it Einstein? Two faces go through the scanner of a network that learned only Einstein: Haythem → NO, Einstein → YES. Watching is free; touching is Premium.
#embeddings-3d — A word becomes a vector: close in space = close in meaning, and you can do math on them.

Learn from labelled examples: regression, classification, trees, neighbors, margins and honest metrics.

#live-training — Six algorithms learning before your eyes, like a video: REC, timecode, subtitles, live metrics. Watching is free; touching the model is Premium.
#linear-regression — Fit a line: least squares, residuals, MSE, R² and gradient descent — the first brick of every supervised model.
#logistic-regression — Classify into two categories: sigmoid, decision boundary, threshold and log-loss — and why a line is not always enough.
#decision-trees — A tree that carves the plane into rectangles: Gini, entropy, depth, pruning — and the overfitting you can see with your own eyes.
#knn — k nearest neighbours: classify by resemblance, pick k, change the distance — and watch the boundary smooth out or shatter.
#svm-margins — Support vector machines: the widest possible margin, the C parameter, and the RBF kernel that curves the boundary.
#classification-metrics — Precision, recall, F1, confusion matrix, ROC and AUC: reading a classifier honestly, especially when classes are imbalanced.

Find structure without labels: clustering, dimensionality reduction, density and anomalies.

#k-means — Grouping without labels: centroids that move, inertia that drops, the choice of k — and the shapes where k-means fails.
#pca — Principal component analysis: find the axes where data varies most, project, compress — and measure what is lost.
#hierarchical-clustering — Merge the points two at a time until only one is left: the dendrogram, the linkage criteria, and the cut height that decides the number of clusters.
#dbscan — Group by density: epsilon, MinPts, core, border and noise points — the algorithm that finds arbitrary shapes and ignores intruders.
#anomaly-detection — Spot what fits nothing: z-score / Mahalanobis, Isolation Forest, LOF — three ways to say 'this point is odd'.
#t-sne-umap — Map the high dimensions: t-SNE and UMAP unfold 10-dimensional data into a readable 2D map — perplexity, neighbors, and reading pitfalls.

Train deep networks: optimizers, normalization, sequences, autoencoders, transfer and generative models.

#optimizers — SGD, Momentum and Adam: the race to the minimum.
#batch-normalization — Batch normalization: keeping activations in the right range.
#rnn-lstm — RNN and LSTM: remembering a sequence.
#autoencoder — Autoencoder: compress then reconstruct.
#transfer-learning — Transfer learning: start from an already-trained network.
#gan — GAN: a forger against an inspector

How a large language model reads, weighs and generates text: tokens, attention, temperature, RAG and alignment.

#tokenization — Tokenization and BPE: how an LLM chops up text.
#attention — Self-attention: each word looks at the others.
#transformer-block — The transformer block: attention, residuals, normalization, FFN.
#generation-temperature — Generation: temperature, top-k and top-p.
#rag — RAG: answering from your documents.
#alignment-rlhf — Alignment and RLHF: learning what humans prefer.

Learn by trial and reward: bandits, Q-learning and policies.

Make an LLM act: ReAct loop, tool calling (MCP), Claude Code / Cursor style slash commands, memory, planning and multi-agent orchestration.

#react-loop — An agent's ReAct loop: Thought → Action → Observation, live.
#tool-calling — Tool calling (function calling / MCP): the JSON that makes the LLM act.
#slash-commands — Slash commands, Claude Code / Cursor style: templates, arguments, chaining.
#context-memory — Context window and agent memory: count, truncate, summarize, index.
#planning-reflection — Planning, reflection and self-correction: from 60% to 90% success.
#multi-agents — Multi-agents: planner, workers, verifier. A DAG that beats the monolithic agent.