Pinned Repositories
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Author_Recog_Kaggle
bert-qa
BERT for question answering starting with HotpotQA
bi-att-flow
Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.
codesnip
代码片段
DoodleRecognition
keras
Deep Learning for humans
natural-questions
Parser-v3
Stanford CoNLL 2018 Graph-based Dependency Parser
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Ambermeow's Repositories
Ambermeow/-
Ambermeow/Author_Recog_Kaggle
Ambermeow/bert-qa
BERT for question answering starting with HotpotQA
Ambermeow/bi-att-flow
Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.
Ambermeow/codesnip
代码片段
Ambermeow/DoodleRecognition
Ambermeow/keras
Deep Learning for humans
Ambermeow/natural-questions
Ambermeow/Parser-v3
Stanford CoNLL 2018 Graph-based Dependency Parser
Ambermeow/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Ambermeow/Using-F-score-to-evaluate-the-LSTM-model
It seems that one can plot the beautiful predicting curves in lookback testing using LSTM. However, the most important thing for individual clients, is whether the price would move up or down, instead of the absolute deviation. So, here I used F score to evaluate that performance of LSTM in lookback testing.