Search Results for "text generation"

NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare...

...The repository begins with a simple character-level bigram language model and develops toward a GPT-style transformer. Its compact Python code exposes core ideas such as tokenization, embeddings, attention, transformer blocks, training, and text generation. The Git history mirrors much of the progression shown during the lecture. The implementation favors educational clarity over production optimization and includes known limitations in areas such as weight initialization. It therefore serves as a practical bridge between introductory language modeling and larger GPT implementations.

...The repository covers core neural network concepts such as weights, biases, activation functions, and gradient descent, as well as more advanced techniques like convolutional networks, recurrent networks, and reinforcement learning. It includes multiple hands-on projects, such as handwritten digit recognition, airplane detection in images, and text generation using recurrent neural networks, which demonstrate how different architectures solve real-world problems.

The *Tree-to-Tree (t2t) Alignment Pipe* is a collection of python scripts, co-ordinating the process of automatic alignment of parallel treebanks from plain text files with a single call from a unix command line. Supported Languages: DE, FR, EN