Search Packages

easyocr

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

onnx2tf

A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.

ethnicolr

Predict Race and Ethnicity Based on the Sequence of Characters in a Name

scalecast

The practitioner's forecasting library

base-attentive

Modular Multi-Backend Encoder-Decoder Architecture for Probabilistic Time Series Forecasting

melpy

A NumPy-based deep learning library for building neural networks. It features an automatic differentiation engine and supports training architectures like LSTM, CNN, and FNN.

phi-3-vision-mlx

Phi-3.5 for Mac: Locally-run Vision and Language Models for Apple Silicon

pyadlml

Sklearn flavored library containing numerous Activity of Daily Livings datasets, preprocessing methods, visualizations and models.

accelbrainbeat

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

morphseg

An efficient and easy-to-use morpheme segmentation library

pydbm

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

numpy-ml

Machine learning, in numpy

pysummarization

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

memory-gym

A gym that contains the memory benchmarks Mortar Mayhem, Mystery Maze and Searing Spotlights

tsad

Time Series Analysis for Simulation of Technological Processes

pyqlearning

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

tsfedl

Time Series Feature Extraction using Deep Learning

stocker

Stock Price Prediction

mlproto

Modular Prototyping Tool for LSTM Machine Learning Models

stockdl

Predicts the Gross Yield, Annual Yield and Net Yield of a user given stock ticker.

see-rnn

RNN and general weights, gradients, & activations visualization in Keras & TensorFlow

kulc

Keras Utility & Layer Collection.

ceemdan-lstm

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

accel-brain-base

accel-brain-base is a basic library of the Deep Learning for rapid development at low cost. This library makes it possible to design and implement deep learning, which must be configured as a complex system, by combining a plurality of functionally differentiated modules such as a Deep Boltzmann Machines(DBMs), an Auto-Encoder, an Encoder/Decoder based on Long Short-Term Memory(LSTM), a Convolutional Auto-Encoder(CAE), and Transformer.