attention-mechanism

Curated implementation notebooks and scripts of deep learning based natural language processing tasks and challenges in TensorFlow.

The notebook explains the various steps to obtain the results of publication: "Is Space-Time Attention All You Need for Video Understanding?"

A set of notebooks that explores the power of Recurrent Neural Networks (RNNs), with a focus on LSTM, BiLSTM, seq2seq, and Attention.

micrograd, cs224n & cs336 assignments + algebra, probability, numpy, pytorch notebooks refresh

This repository contains Jupyter Notebook Files of some state of the art projects that I completed during my internship program in deeplearning.ai. The project files are divided into 5 main categories or respective courses that the deeplearning.ai provides.

CNN Pooling, Convolution ve Attention mekanizmalarının interaktif Jupyter notebook demonstrasyonu

This repository contains codes and Jupyter notebooks exploring Transformers architecture

Jupyter Notebook tutorial on Attention Mechanisms, Position Embeddings and Random Fourier Feature based approximations. Created for Imperial College Deep Learning Course

Educational notebooks that demystify Large Language Models and Computer Vision. We build everything from scratch — from a simple bigram language model to RNNs, LSTMs, Attention, Transformers, CNNs, and Diffusion models (DDPM) — using pure Python and PyTorch. No hype. Just code.

A hands-on repository documenting my experiments, notebooks, and notes as I learn and apply deep learning concepts through practice.

Implementation notebooks and scripts of Artistic CNN Models and Generative Models like GANs, VAEs, GMMs, Boltzmann Machine etc. in TensorFlow, and Python. This repo aims to understand and make amazing things out of Neural Network layers.

notebooks for lessons on NLP bonus stream of OTUS course "ML.Advanced"

Attention Meta-Learner for MOEX Si futures — thesis, research notebook, backtest and Streamlit dashboard

Educational PyTorch notebooks exploring self-attention, causal masking, multi-head attention and efficient attention variants.

This colab`s notebook contains pipeline for creating one-to-one machine translation model based on Seq2SeqTransformer

A walkthrough that builds a Transformer from first principles inside Jupyter notebooks — focusing on clarity, correctness, and intuition.

A hands-on guide to understanding and building Transformer models from scratch, with detailed explanations and practical Jupyter notebooks.

Explainable AI wind speed forecasting: CNN-BiLSTM-Attention in PyTorch explained with SHAP, LIME and attention. Notebook, live demo, synthetic data.

Automated 8-class peripheral blood cell classification on BloodMNIST using SE-ResNet-18 (97.23% accuracy). Includes full IEEE report & Colab notebook.

WuWeiDL: Daoist-inspired turbulence-gated attention for wind speed forecasting in PyTorch. Wu Wei gate, Feng-Qi encoding, notebook and live demo.