AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
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May 9, 2021 - Python
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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Build your neural network easy and fast, 莫烦Python中文教学
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
CNN-RNN中文文本分类,基于TensorFlow
中文古诗自动作诗机器人,屌炸天,基于tensorflow1.10 api,正在积极维护升级中,快star,保持更新!
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
Deep learning driven jazz generation using Keras & Theano!
Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
Recurrent neural network for audio noise reduction
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
1st place solution
Datasets, tools, and benchmarks for representation learning of code.
Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.
Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
TensorFlow Tutorial for Time Series Prediction
Data augmentation for NLP, presented at EMNLP 2019
Sentiment Analysis with LSTMs in Tensorflow
Chatbot in 200 lines of code using TensorLayer
RNN based Time-series Anomaly detector model implemented in Pytorch.
Stock Price Prediction using Machine Learning Techniques
Tensorflow implementation of attention mechanism for text classification tasks.
Sequence to Sequence Models with PyTorch
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
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Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))