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.

scalecast

The practitioner's forecasting library

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.

cmne

Contextual MEG/EEG source estimation: an LSTM learns the temporal context of brain activity to sharpen dSPM/MNE estimates. PyTorch + ONNX, MNE-Python compatible.

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.

parsernaam

AI name parsing. Predict first or last name using a DL model.

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.

see-rnn

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

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.

numpy-ml

Machine learning, in numpy

nano-ocr

A Lightweight OCR model and Engine for low computation devices, edge applications and embedded system

tsfedl

Time Series Feature Extraction using Deep Learning

mlproto

Modular Prototyping Tool for LSTM Machine Learning Models

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.

morphseg

An efficient and easy-to-use morpheme segmentation library

deepalpha-freqai

AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.

ceemdan-lstm

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

easyocr-itgn

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

tsad

Package for Time Series Forecasting and Anomaly Detection Problems.

base-attentive

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

memory-gym

Challenging Memory-based Deep Reinforcement Learning Agents

seq2seqsharp

Seq2SeqSharp is a tensor based fast & flexible deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, different network types (Transformer, LSTM, BiLSTM and so on), multi-GPUs supported, cross-platforms (Windows, Linux, MacOS), multimodal model for text and images and so on.