Interactive Catalog.
Explore all 18 topics through visual, interactive lessons. See the inner workings of deep learning concepts up close.
Deep Learning Foundations & Training
Single Neuron Forward Pass
Input -> weights -> bias -> activation -> output. See how one neuron turns features into a prediction signal.
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Activation Functions Comparison
Compare ReLU, Leaky ReLU, Sigmoid, and Tanh to see how nonlinearities shape a neuron response.
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Loss Functions
Compare prediction with truth, convert error into a scalar loss, and understand how loss becomes the training signal.
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Backpropagation
Follow the error signal backward through cached forward values, gradients, and parameter updates.
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Gradient Descent & Learning Rate
Watch parameters move across a loss surface and see how learning rate changes the descent path.
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Overfitting vs. Underfitting
Adjust model complexity, data size, noise, and regularization to diagnose underfit, good fit, and overfit behavior.
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Dropout
See how random neuron masking and inverted dropout scaling reduce co-adaptation during training.
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Adam Optimizer vs. SGD
Compare SGD, momentum, and Adam on a 3D loss surface to see how optimizer dynamics differ.
Explore topic →Vision & Sequence Models
Convolution Operation
Watch a filter slide across an input image, multiply local patches, and produce an output feature map.
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Pooling and Downsampling
Compare max pooling and average pooling as a window slides across a feature map and reduces spatial size.
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Feature Map Visualization
See how different filters and CNN layers produce different activation maps that highlight edges, textures, and patterns.
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RNN Structure
Compare one-to-one, one-to-many, many-to-one, and many-to-many RNN layouts for sequence tasks.
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Attention Mechanism Intuition
Follow tokens through embeddings, Query/Key/Value projections, attention scores, softmax weights, and context vectors.
Explore topic →Model Development Toolkit
Train / Val / Test Split
Understand how data is split for training, tuning, and final evaluation while avoiding leakage.
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Evaluation Metrics & Confusion Matrix
Move a classification threshold and see how TP, FP, TN, FN, accuracy, precision, recall, and F1 change.
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Bias vs. Variance Diagnosis
Compare human-level, training, train-dev, dev, and test errors to diagnose bias, variance, mismatch, and dev-set overfitting.
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Mini-batch Training & Batch Size
Compare full-batch, mini-batch, and SGD updates to understand why training paths can be smooth or noisy.
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Transfer Learning Intuition
See how a model pretrained on Task A can reuse learned features, replace the head, and adapt to a smaller Task B.
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