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  An RNN, or   Recurrent Neural Network , artificial neural network - sequential data - information from previous inputs. stock prices,  Natural Language Processing (NLP):  Used for tasks like email autocomplete, language translation, sentiment analysis, and named entity recognition. Time series forecasting:  Predicting future values in a sequence, such as stock prices or weather patterns. Speech recognition:  Understanding and transcribing spoken language. Video analysis:  Understanding the content of video sequences.   Types of RNNs

  How a transformer can live outside Python Compiled inference runtimes – a pretrained model can be exported to a platform‑independent format (ONNX, TensorRT, OpenVINO, Core ML, etc.). Those files are then loaded by a lightweight C/C++ runtime that does the matrix math, so the only “installation” is the runtime library. Native code implementations – you can write the transformer architecture yourself in C++, Rust, Java, Go, etc., and link it with a BLAS/LBLAS library (Intel MKL, OpenBLAS, etc.). The model weights are just numbers that you read from a file. Hardware‑specific pipelines – some chips (GPUs, TPUs, neuromorphic boards) have their own SDKs that accept a transformer graph and run it without any Python stack. No‑code platforms – services like Hugging Face Inference API, AWS SageMaker, Google Vertex AI, or Azure OpenAI let you call a transformer via a REST endpoint; you never install anything locally.