CNN
Pure NumPy implementation of a tiny convolutional neural network (CNN) designed for education and experimentation. The project shows every moving piece needed to train an image classifier end to end: data loading, convolutions, activation functions, multilayer perceptron heads, training loops, and evaluation metrics
What XERJ indexed
Selected work
Licence
The repository
LICENSE file checked at the pinned commit; the author agreed to inclusion.
The XERJ corpus
The owner added it on hub.xerj.org after signing in with GitHub, on 2026-10-29. Same terms as the repository. Answers name the repo, the commit and the file:line.
selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution
The prompt
Paste it into your AI agent. It clones Tuhin-SnapD/CNN at the pinned commit and indexes it on your machine.
Search the GitHub repository Tuhin-SnapD/CNN with XERJ (https://hub.xerj.org/corpus/tuhin-snapd--cnn). Run these in a shell, in order, and check each result before the next: 1. xerj --help (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh 2. git clone https://github.com/Tuhin-SnapD/CNN CNN && git -C CNN checkout 040ac534ddb7cc4a00c090d8968e065a69484600 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./CNN --url http://127.0.0.1:9200 --prefix cnn --progress plain 5. xerj autoindex map --url http://127.0.0.1:9200 Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt Licence: MIT. Keep the copyright notice and credit the authors.
Related
Tuhin ChakrabartyNetworking, protocols and serialisation corpora
Stats: GitHub API, fetched 2026-10-29. Selected work and consent: the XERJ registry.