Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
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Updated
Jul 28, 2022 - Python
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.
Char-RNN implemented using TensorFlow.
Implementing Recurrent Neural Network from Scratch
Little More Advanced TensorFlow Implementations
Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
Hands-On Deep Learning Algorithms with Python, By Packt
Chinese Poetry Generation
e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond
Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.
medium blog supplementaries | Backprop | Resnet & ResNext | RNN |
The implementation of LSTM in TensorFlow used for the stock prediction.
[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection
Char-level RNN LSTM text generator📄.
This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep networ…
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
Predict stock movement with Machine Learning and Deep Learning algorithms
Implementation of a series of Neural Network architectures in TensorFow 2.0
Voice Activity Detection LSTM-RNN learning model
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