Interactive Playground
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No PhD. No gatekeeping. Just clear explanations, visual intuitions, and hands-on code — from perceptrons to transformers.
Every concept gets an interactive visualization before a single line of math. See gradients flow. Watch neurons activate. Understand before you memorize.
Every lesson includes runnable Python code. No setup needed — run examples directly in your browser with our built-in playground.
Follow structured learning paths from absolute beginner to building production models. No more drowning in random YouTube videos.
No paywalls. No "premium" tiers hiding the good stuff. Quality AI education for everyone, everywhere.
Pick your path. Each one is structured to take you from "what is this?" to "I built that."
The complete beginner path. Learn how neural networks actually work, from a single neuron to a full network that classifies images.
Teach machines to see. Build CNNs that recognize objects, detect faces, and understand images.
From bag-of-words to attention mechanisms. Understand how GPT and BERT actually work under the hood.
Understand the magic behind image generation, chatbots, and creative AI. From VAEs to Diffusion Models.
Linear regression, loss functions, overfitting, cross-validation
12 lessonsPerceptrons, MLPs, activation functions, weight initialization
15 lessonsSGD, Adam, learning rate schedules, batch normalization
10 lessonsConvolutions, pooling, ResNet, object detection
11 lessonsSequence models, vanishing gradients, LSTM gates explained
8 lessonsSelf-attention, multi-head attention, positional encoding, BERT, GPT
9 lessonsGANs, VAEs, diffusion models, prompt engineering
7 lessonsQ-learning, policy gradients, environments, reward shaping
6 lessonsPyTorch, TensorFlow, HuggingFace, Jupyter, Colab
8 lessonsNo setup. No pip install. Just write, run, and learn. Powered by Pyodide.
The algorithm that teaches a neural network by calculating how much each weight contributed to the error, then adjusting accordingly. Think of it as "credit assignment."
Learn more →When a model memorizes the training data instead of learning general patterns. It aces the exam it studied but fails every new test.
Learn more →A way for a model to focus on the most relevant parts of input when making predictions — the core idea behind transformers and GPT.
Learn more →One complete pass through your entire training dataset. Models usually need many epochs to converge to a good solution.
Learn more →A way to represent something (a word, image, user) as a dense vector of numbers in a high-dimensional space where similar things are close together.
Learn more →A regularization trick where random neurons are "turned off" during training, forcing the network to not rely on any single neuron. Makes it more robust.
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