Rock paper scissors against a Hedge ensemble that learns your habits — beats patterned players 80-100% while correctly settling at chance against true randomness.
Makine öğrenmesi ile sayı dizilerini analiz eden, örüntü tespiti yapan ve farklı modelleri karşılaştırmaya odaklanan Python kütüphanesi.
NuMA: compact local GRU text autocomplete and number-sequence reasoners with transparent reasoning and a chat interface.
TokenTrie: Real-time sequence prediction engine built on Dynamic Suffix Tries. Implements Context Mixing and Entropy Scaling to capture variable-order dependencies without neural networks.
Baseball pitch sequence prediction using 7 ML models (LSTM, Transformer, CNN, HMM, Random Forest, Logistic Regression, AutoGluon) with synthetic data generation, k-fold benchmarking, ablation studies, and MLflow tracking.
Deep learning-driven cache eviction policy using LSTM forecasting and uncertainty estimation, deployed as a production microservice with a full MLOps pipeline.
日志异常检测，Used for log anomaly detection, including log processing, training, prediction, and output results.
Предсказание последовательностей цифр по заданному правилу с помощью RNN, LSTM и GRU на PyTorch.
PyTorch implementations of MLP, CNN, and RNN for deep learning coursework, including comparisons on synthetic datasets, CIFAR-10 classification, and palindrome prediction tasks with training scripts and notebooks for analysis.
Recurrent Neural Network (RNN) implementation for time-series forecasting. Demonstrates temporal sequence prediction using a SimpleRNN architecture in TensorFlow/Keras.
This analysis demonstrates building and training a simple RNN model to predict the next number in a sequence, covering data preparation, model architecture, training, and prediction.
Predicting likely 1D layer-depth sequences based on formations where lateral wells are typical
Predict next number in a sequence using a simple ANN. Modularized code with classes for data preparation, neural network architecture, and training.
Persian news generation using GRU units
Pytorch Implementation of DeepLog.
The repository includes implementations of quaternion networks and new QALE loss function, which calculates the error value based on the difference in angles between the result and the expected value. Procedures for performing the training and evaluation of predicting successive elements of a rotation sequence are also provided.
Four digit SVHN (Street View House Number) sequence prediction with CNN using Keras with TensorFlow backend
A LSTM model to predict the next Fibonacci number.
Transformer-based Implementation of DeepLog.
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