機械学習 · GitHub

Tensorflow-based CNN+LSTM trained with CTC-loss for OCR

💻 📓 Guia de estudos (iniciante) sobre Inteligência Artificial. Contém trilhas de aprendizagem, canais, cursos , livros etc.

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Re-implement Kaiming He's deep residual networks in tensorflow. Can be trained with cifar10.

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark

A deep learning toolkit for mass spectrometry

tensorflow and theano cnn code for insurance QA(question Answer matching)

Deep learning for molecules and materials book

Fast Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

• Various units: Stochastic binary, Gaussian, Softmax and nRLU units
• CD-1 learning by default

🔥🔥The pytorch implement of the head pose estimation(yaw,roll,pitch) and emotion detection with SOTA performance in real time.Easy to deploy, easy to use, and high accuracy.Solve all problems of face detection at one time.(极简，极快，高效是我们的宗旨)

MiVOLO age & gender transformer neural network

Python framework for Deep Learning in Proteomics

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms

Machine Learning Experiments and Work