{ "graph": { "nodes": [ { "id": "gradient_descent", "kind": "concept", "label": "Gradient Descent", "summary": "Iterative optimization that steps parameters downhill along the loss gradient." }, { "id": "backpropagation", "kind": "concept", "label": "Backpropagation", "summary": "Reverse-mode autodiff that computes loss gradients through a network." }, { "id": "chain_rule", "kind": "concept", "label": "Chain Rule", "summary": "Calculus rule for differentiating composed functions." }, { "id": "neural_network", "kind": "concept", "label": "Neural Network", "summary": "Layered function approximator built from weighted connections." }, { "id": "loss_function", "kind": "concept", "label": "Loss Function", "summary": "Scalar objective that quantifies prediction error." }, { "id": "learning_rate", "kind": "concept", "label": "Learning Rate", "summary": "Step-size hyperparameter scaling each gradient update." }, { "id": "overfitting", "kind": "concept", "label": "Overfitting", "summary": "When a model memorizes noise instead of generalizable signal." }, { "id": "regularization", "kind": "concept", "label": "Regularization", "summary": "Techniques that penalize complexity to improve generalization." } ], "edges": [ { "id": "e1", "source": "chain_rule", "target": "backpropagation", "type": "prerequisite_of", "label": "underlies" }, { "id": "e2", "source": "backpropagation", "target": "neural_network", "type": "explains", "label": "trains" }, { "id": "e3", "source": "gradient_descent", "target": "loss_function", "type": "related_to", "label": "minimizes" }, { "id": "e4", "source": "loss_function", "target": "gradient_descent", "type": "prerequisite_of", "label": "defines objective for" }, { "id": "e5", "source": "learning_rate", "target": "gradient_descent", "type": "related_to", "label": "tunes" }, { "id": "e6", "source": "gradient_descent", "target": "backpropagation", "type": "related_to", "label": "consumes gradients from" }, { "id": "e7", "source": "neural_network", "target": "overfitting", "type": "related_to", "label": "can suffer" }, { "id": "e8", "source": "regularization", "target": "overfitting", "type": "contradicts", "label": "counteracts" }, { "id": "e9", "source": "regularization", "target": "neural_network", "type": "related_to", "label": "constrains" }, { "id": "e10", "source": "loss_function", "target": "neural_network", "type": "related_to", "label": "evaluates" }, { "id": "e11", "source": "learning_rate", "target": "loss_function", "type": "related_to", "label": "affects descent on" }, { "id": "e12", "source": "chain_rule", "target": "gradient_descent", "type": "related_to", "label": "supports" }, { "id": "h1", "source": "learning_rate", "target": "overfitting", "type": "related_to", "label": "hypothesis: high rate may worsen", "properties": { "edge_role": "hypothesis" } } ], "claims": [ { "id": "c1", "claim_type": "note", "target_id": "gradient_descent", "text": "The optimization workhorse behind most deep learning training.", "origin": "user_asserted", "support_state": "source_supported", "review_state": "accepted" }, { "id": "c2", "claim_type": "note", "target_id": "backpropagation", "text": "Makes gradient computation efficient via reverse-mode automatic differentiation.", "origin": "user_asserted", "support_state": "unverified", "review_state": "pending" }, { "id": "c3", "claim_type": "note", "target_id": "overfitting", "text": "Gemma inferred this is a relevant failure mode for the networks discussed here — a hypothesis awaiting your review.", "origin": "model_inferred", "support_state": "unverified", "review_state": "pending" }, { "id": "n1", "claim_type": "note", "target_id": "regularization", "text": "Dropout and weight decay are the most common regularization techniques here.", "origin": "user_asserted", "support_state": "unverified", "review_state": "pending" }, { "id": "hyp1", "claim_type": "hypothesis", "target_id": "learning_rate", "text": "Hypothesis: an overly large learning rate amplifies overfitting on small datasets.", "origin": "user_asserted", "support_state": "unverified", "review_state": "pending" }, { "id": "hyp2", "claim_type": "hypothesis", "target_id": "h1", "text": "Hypothesis edge: learning rate and overfitting may be causally linked.", "origin": "model_inferred", "support_state": "unverified", "review_state": "pending" }, { "id": "q1", "claim_type": "question", "target_id": "loss_function", "text": "Open question: which loss best balances calibration and accuracy for this task?", "origin": "user_asserted", "support_state": "unverified", "review_state": "pending" }, { "id": "q2", "claim_type": "question", "target_id": "chain_rule", "text": "Open question: how to explain the chain rule intuition without calculus prerequisites?", "origin": "user_asserted", "support_state": "unverified", "review_state": "pending" } ], "metadata": { "schema_version": "0.1.0", "node_count": 8, "edge_count": 13 } }, "scene": { "scene_id": "annotated_graph_initial", "visible_node_ids": [ "gradient_descent", "backpropagation", "chain_rule", "neural_network", "loss_function", "learning_rate", "overfitting" ], "visible_edge_ids": ["e1", "e2", "e3", "e4", "e5", "e6", "e7", "e10", "e11", "e12"], "fogged_node_ids": ["regularization"], "highlighted_ids": [], "selected_id": "", "callouts": [ { "id": "co1", "target_id": "learning_rate", "text": "Open question attached: tuning vs. overfitting." } ], "node_positions": { "gradient_descent": [0.0, 0.0], "backpropagation": [200.0, -80.0], "chain_rule": [400.0, -40.0], "neural_network": [120.0, 180.0], "loss_function": [-180.0, 120.0], "learning_rate": [-120.0, -160.0], "overfitting": [360.0, 150.0] } } }