Modular IMDb sentiment classification comparing classical ML and PyTorch neural models, with reproducible experiments, smoke-test validation, evaluation, and structured reporting.
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Aug 25, 2026 - Python
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Modular IMDb sentiment classification comparing classical ML and PyTorch neural models, with reproducible experiments, smoke-test validation, evaluation, and structured reporting.
Host a Freeciv longturn multiplayer server with live status, email alerts, and AI-generated newspaper updates
The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516
In this work, we shared the source code which are utilized in the study named ODE Transformers for Isolated Sign Language Recognition: A Study on Robustness and Scalability. This code implements the multimodal experiments which reported in the article.
A from-scratch PyTorch implementation of trainable self-attention, including Q/K/V projections, scaled dot-product attention, backpropagation, and contextual embeddings.
SASRec с нуля на PyTorch (causal self-attention вручную) на Yandex Yambda. Аудио-эмбеддинги квантованы в semantic IDs через RQ-VAE: как замена item-эмбеддингов проигрывают, как дополнение дают +39% к NDCG@10 (0.575 vs 0.413). Ablation из шести моделей
A decoder-only Transformer implemented from scratch in PyTorch for character-level name generation.
臺灣大學 (NTU) 李宏毅教授「機器學習 (Machine Learning) 2021 Spring 」課程筆記
Lekka warstwa PyTorch dodająca globalny schemat i adaptacyjną kontrolę do Transformerów (O(n) zamiast O(n²)). Pozwala modelowi zobaczyć ogólny obraz sekwencji przed analizą szczegółów.
LLM 推理性能决策基线:TTFT/TPOT、KV Cache、吞吐与解码策略对照
Official PyTorch Implementation of "Deconver: A Deconvolutional Network for Medical Image Segmentation"
Watch a transformer block as a living agent society.
GPT-style decoder-only Transformer built from scratch in PyTorch — custom byte-level BPE tokenizer, causal multi-head self-attention, Pre-LN Transformer blocks, and end-to-end training on WikiText-2. 9 experiments reduced test perplexity 120.79 → 73.76, with an 8.6M-parameter model outperforming larger runs.
PyTorch implementation of the FT-Transformer (Gorishniy et al., 2021) for tabular regression — benchmarked against classical baselines on California Housing, with ablation studies, Optuna tuning, 98% test coverage, and CI/CD.
ECG heartbeat image classification with CNN baselines, ECG-specific augmentation, and ResNet-18 with spatial self-attention.
Interactive explorer for the self-attention of a GPT I built from scratch and trained on my own poetry
A transformer attention head running on an FPGA in integer math
A pipeline for the analysis of Bert self-attention mechanism applied to proteins.
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