An educational crypto, finance, and AI vocabulary puzzle about data structures, business records, model architectures, and data processing.
Blockchain Data Structures
These structures organize linked history, transaction commitments, and pending transactions.
Linked ListMerkle TreePatricia TrieTransaction Pool
- Linked List: A linked list organizes records through references from one record to another.
- Merkle Tree: A Merkle tree combines hashes in layers so a root hash commits to the underlying records.
- Patricia Trie: A Patricia trie is a compressed prefix tree used to organize keys and their associated values.
- Transaction Pool: A transaction pool holds transactions awaiting inclusion in a block.
Accounting Source Documents
Source documents record business transactions and support the preparation of accounting records.
Sales InvoicePurchase OrderPayment ReceiptCredit Note
- Sales Invoice: A sales invoice records goods or services sold and the amount billed to the customer.
- Purchase Order: A purchase order specifies goods or services a buyer requests from a supplier.
- Payment Receipt: A payment receipt acknowledges that a payment has been received.
- Credit Note: A credit note records a reduction in an amount previously billed, such as for returned goods.
Neural Network Architectures
Neural network architectures organize computations in different ways for processing and representing data.
Convolutional NetworkRecurrent NetworkTransformerAutoencoder
- Convolutional Network: A convolutional network uses learned filters to detect local patterns in data such as images.
- Recurrent Network: A recurrent network carries information across sequence steps through recurrent connections.
- Transformer: A transformer uses attention mechanisms to combine information from different positions in its input.
- Autoencoder: An autoencoder learns an encoded representation by training a decoder to reconstruct its input.
Data Pipeline Stages
Data pipelines collect information, correct quality issues, change representations, and write results to a destination.
Data IngestionData CleaningData TransformationData Loading
- Data Ingestion: Data ingestion brings data from source systems into a processing or storage environment.
- Data Cleaning: Data cleaning identifies and handles issues such as invalid values, missing fields, and duplicate records.
- Data Transformation: Data transformation changes the structure, format, or values of data for a downstream use.
- Data Loading: Data loading writes prepared data into a destination such as a database or warehouse.