Lstm Python 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

phi-3-vision-mlx

Phi-3-Vision on Apple silicon with MLX

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.

numpy-ml

Machine learning, in numpy

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.

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.

deepalpha-freqai

Quant-grade ML plugin for Freqtrade/FreqAI: Triple Barrier labels, SHAP selection, meta-labeling, purged walk-forward CV, LightGBM.

tsad

Package for Time Series Forecasting and Anomaly Detection Problems.

pyadlml

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

tsfedl

Time Series Spatio-Temporal Feature Extraction using Deep Learning

morphseg

A multilingual package for segmenting text into morphemes using supervised deep learning.

nlp-uncertainty-zoo

Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.

mlproto

Modular Neural Network Protyping for Stock Market Prediction

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.

accel-brain-base

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.

text-classification-keras

Text Classification Library for Keras

base-attentive

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

spacy-jptdp

Neural network models for joint POS tagging and dependency parsing (CoNLL 2017-2018)

stocker

Stock Price Prediction

stockdl

A financial deep learning library for stocks price prediction and comparison with traditional investment strategies. The Library is based on LSTM-Neural Networks and Conv1D + LSTM Neural Networks. Investments are subject to market risks, The AUTHOR HOLDS NO RESPONSIBILITY for any financial loss.

ceemdan-lstm

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