{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OWL6KED4Y4PHMYDHMMQFP6WEBK","short_pith_number":"pith:OWL6KED4","schema_version":"1.0","canonical_sha256":"7597e5107cc71e766067632057fac40aa5c57ef8f2731d145bb8ab06a61c5845","source":{"kind":"arxiv","id":"1709.01412","version":2},"attestation_state":"computed","paper":{"title":"Deep learning: Technical introduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Thomas Epelbaum","submitted_at":"2017-09-05T14:27:08Z","abstract_excerpt":"This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed. The forward pass and the update rules for the backpropagation algorithm are then derived in full."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1709.01412","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T14:27:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3f20d4b52f39ee800f577df421099398f2e54b90eb66d5ae3b34bae5a310a3af","abstract_canon_sha256":"8a85a59318e8cb88025044027d6d5ce030c24d2635e85de9d5fc436ab8640223"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:40.628980Z","signature_b64":"0SQ/v9IGDRlmu6gpAgtaWQmEArRUoM3J6X8/iNFlOaw9uTTBPzZ6hWJr2H7LPd5i7pq1LkHqoYsj0UmbZvjzBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7597e5107cc71e766067632057fac40aa5c57ef8f2731d145bb8ab06a61c5845","last_reissued_at":"2026-05-18T00:35:40.628273Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:40.628273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning: Technical introduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Thomas Epelbaum","submitted_at":"2017-09-05T14:27:08Z","abstract_excerpt":"This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed. The forward pass and the update rules for the backpropagation algorithm are then derived in full."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01412","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.01412","created_at":"2026-05-18T00:35:40.628382+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01412v2","created_at":"2026-05-18T00:35:40.628382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01412","created_at":"2026-05-18T00:35:40.628382+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWL6KED4Y4PH","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWL6KED4Y4PHMYDH","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWL6KED4","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK","json":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK.json","graph_json":"https://pith.science/api/pith-number/OWL6KED4Y4PHMYDHMMQFP6WEBK/graph.json","events_json":"https://pith.science/api/pith-number/OWL6KED4Y4PHMYDHMMQFP6WEBK/events.json","paper":"https://pith.science/paper/OWL6KED4"},"agent_actions":{"view_html":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK","download_json":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK.json","view_paper":"https://pith.science/paper/OWL6KED4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01412&json=true","fetch_graph":"https://pith.science/api/pith-number/OWL6KED4Y4PHMYDHMMQFP6WEBK/graph.json","fetch_events":"https://pith.science/api/pith-number/OWL6KED4Y4PHMYDHMMQFP6WEBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK/action/storage_attestation","attest_author":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK/action/author_attestation","sign_citation":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK/action/citation_signature","submit_replication":"https://pith.science/pith/OWL6KED4Y4PHMYDHMMQFP6WEBK/action/replication_record"}},"created_at":"2026-05-18T00:35:40.628382+00:00","updated_at":"2026-05-18T00:35:40.628382+00:00"}