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Cudnn 5 now supports GRU and LSTM cells natively, with speedup's of up to 6 times compared to a Cublass implementation of GRU's and LSTMs. IT would be great if Cudarray could support this.
https://de…
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seems very hands on and practical. Check it out:
http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
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1. **Orientation**
Overview of the Tech Society.
*Host: All 2nd and 3rd year members*
2. **Distributed Systems - Basics**
Introduction to scalable servers and tools.
*Host…
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I noticed that the current toolkit only supports Conv2D and Dense models. But in practical application and research, we often use time series data to predict. So I want to know whether this theory is …
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In financial derivatives hedging, feature extraction is locating and extracting useful data or features from raw data that may be utilized to make forecasts or choices. These characteristics may inclu…
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# 호다닥 공부해보는 RNN 친구들(1) - RNN(Recurrent Neural Networks) - 호롤리한 하루
Overview 호다닥 공부해보는 시리즈가 2편째가 되었습니다. 이번에는 머신러닝의 꽃이라고도 불리는 RNN을 들고 왔습니다. 다음 포스팅에서는 RNN친구들인 LSTM과 GRU도 소개하도록 하겠습니다.
[https://gruuuuu.gi…
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Abstract: We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural ne…
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Given that we are working with time series data, we can use LSTM neural networks.
See this group that did weather forecasting with time series data for inspiration:
https://kendrik-wah.github.io/c…
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Research a few possible models we can use to generate text. Possibilities:
* Markov chains: should be relatively easy to implement on our own but likely wouldn't give great results.
* Recurrent ne…