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awesome-llm

Awesome-LLM: a curated list of Large Language Model

awesome-tutorials-for-tensorflow2

cat-imagetextintegrator

An innovative deep learning framework leveraging the CAT (Convolutions, Attention & Transformers) architecture to seamlessly integrate visual and textual modalities. This model exploits the prowess of CNNs for image feature extraction and Transformers for intricate textual pattern recognition, setting a new paradigm in multimodal learning.

data-structures

Data structures implementation in Python

emnist-classifier

Handwritten character recognition on EMNIST ByClass using Convolutional Neural Networks with PyTorch.

mlforce

MLForce stands for Machine Learning Force, which is a Python toolkit for machine learning beginners.

numpymultilayerperceptron

A Multilayer Perceptron from scratch using NumPy. Offers almost all basic functionalities . Suitable for classification and regression tasks. 一个用NumPy从零实现的多层感知机。提供几乎所有基本功能。适用于分类和回归任务。

numpynmf

NumPyNMF implements nine different Non-negative Matrix Factorization (NMF) algorithms using NumPy library and compares the robustness of each algorithm to five various types of noise in real-world data applications.

octis

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

robust-trainers-for-noisy-labels

This experiments with ResNet34 on highly noisy (50%+) FashionMNIST and CIFAR datasets. Focuses on Forward Loss Correction, CoTeaching, initial trials with JoCoR, O2UNet, and Dual-T for unknown transition matrices.

tabtransformers

A PyTorch-based implementation that leverages Transformer architectures to enhance the handling and design of tabular data. With the capabilities of Transformer models, we aim to provide data scientists and machine learning engineers with a flexible tool for more accurate and responsive decision-making.

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