BTC Ai trade bot | Work in progress..
Application of Deep Learning (SNN, CNN, LSTM) and Deep Evidential Learning (Capsa) to create an ensemble based model for stock market prediction (training data - Bitcoin daily chart with Technical Analysis, Pattern Recognition and Fundamental Analysis). Ensemble model shows:
0.9% mean absolute percentage error(mape) on test set (20%) and 2% mape on validation set.
- T5 Transformer Summarization: T5 Seq2Seq model text summarization performance on the XSum dataset. The code is provided as a Colab notebook. https://github.com/MMatulenko/transformer_summarization
- Machine Translation with Seq2Seq Model: This project uses a Seq2Seq model to perform machine translation from English to German. https://github.com/MMatulenko/t5_seq2seq_translation
- Keras IMDB review classification: Simple binary classification model using the IMDB movie review dataset from Keras. https://github.com/MMatulenko/keras_imdb_classification
- Titanic Survival Prediction Predict the survival of passengers aboard the Titanic based on various features such as gender, age, class, and fare. EDA analyze + various model testing (logreg) https://github.com/MMatulenko/Titanic
- Wine Quality multiclass classification: Quality of wine prediction. https://github.com/MMatulenko/Wine_Quality
- Feed Forward NN from scratch: https://github.com/MMatulenko/mm_data_science/blob/main/ffnn_from_scratch.ipynb
- Liquid Neural Network - robustness without overparametrisation https://github.com/MMatulenko/LNN
- RL Lunar simple ai, Pacman with Convolution layer, A3C for Kung Fu https://github.com/MMatulenko/RL/tree/main
♜ Other works: https://github.com/MMatulenko/mm_data_science/
♗ Currently: Deep learning, NLP and AGI
♘ I’m looking to collaborate on Large Language Models (LLM) and Artificial general intelligence.
♙ How to reach me: [email protected] | [email protected]