Popular GitHub Repositories for Conv Layer

The Memory layer for AI Agents

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.

Supercharge Your LLM with the Fastest KV Cache Layer

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Keras Attention Layer (Luong and Bahdanau scores).

LiveStore is a next-generation state management framework based on reactive SQLite and built-in sync engine.

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

An AI memory layer with short- and long-term storage, semantic clustering, and optional memory decay for context-aware applications.

Implementation of the Point Transformer layer, in Pytorch

Draw multi-layer neural network in your browser

Building Convolutional Neural Networks From Scratch using NumPy

TensorFlow Node.js provides idiomatic JavaScript language bindings and a high layer API for Node.js users. TensorFlow Node.js provides idiomatic JavaScript language bindings and a high layer API for Node.js users.

Tools for understanding how transformer predictions are built layer-by-layer

MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management, including MongoDB integration and OpenAI embeddings for semantic search capabilities.

FFmate is a modern and powerful automation layer built on top of FFmpeg - designed to make video and audio transcoding simpler, smarter, and easier to integrate

ChineseNER based on BERT, with BiLSTM+CRF layer

Keras Layer implementation of Attention for Sequential models