Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using Python Libraries) #abdsc

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
Continue reading “Deep Learning Cheat Sheet (using Python Libraries)”

Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using #Python Libraries) | @DataScienceCtrl  #Keras #TensorFlow

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
Continue reading “Deep Learning Cheat Sheet (using Python Libraries)”

GitHub

End-to-end automatic speech recognition from scratch in #Tensorflow  #NeuralNetworks

  • This is a powerful library for automatic speech recognition, it is implemented in TensorFlow and support training with CPU/GPU.
  • The original TIMIT database contains 6300 utterances, but we find the ‘SA’ audio files occurs many times, it will lead bad bias for our speech recognition system.
  • Therefore, we removed the all ‘SA’ files from the original dataset and attain the new TIMIT dataset, which contains only 5040 utterances including 3696 standard training set and 1344 test set.
  • Automatic Speech Recognition transcribes a raw audio file into character sequences; the preprocessing stage converts a raw audio file into feature vectors of several frames.
  • In other words, each audio file is split into frames using the Hamming windows function, and each frame is extracted to a feature vector of length 39 (to attain a feature vector of different length, modify the settings in the file timit_preprocess.

Automatic_Speech_Recognition – End-to-end automatic speech recognition from scratch in Tensorflow
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