The benefits of using Tensorflow promptly Group (Real time)

We’re going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data.

Code for this video:
https://github.com/llSourcell/rnn_tutorial

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More learning resources:
https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/
https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb

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20 Antworten auf „The benefits of using Tensorflow promptly Group (Real time)“

  1. Great model!!! almost 100% accuracy. I liked how you basically created your own RNN instead of using the Keras API.. I feel like when you're on that level, you're ready [or almost ready] to write your own library.

  2. )))) Siraj, stop deleting comments, you can't hide the truth. Your explanation is the worsted I ever heart. You even don't understand the difference between series and time-series data. You just copy-past article from medium and can't understand what other person made.

  3. Great work… I am solving a problem where I have to forecast demand, but it has low volume i.e sales of the product is like like 8 per month or 10… But they are very expensive… So I want to forecast low volume and high price items from a sales database… Please advise

  4. Hi gurus,
    in the code, I see a list holding all states (15 of them) per session.run() is initialized prior to the forward for loop.

    states_series = []

    This is not a tensorflow object but a list holding tensorflow objects. So when the session.run() is executed for the second time, will this list be emptied out automatically because all tensors of previous session.run() were destroyed?

  5. One who using latest version of Tensorflow should make following changes in the above code,
    replace "tf.unpack()" with "tf.unstack ()", "tf.concat(1, [current_input, current_state])" with "tf.concat([current_input, current_state], 1)", "tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)" with "tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)"

  6. Dude, you are very smart and intelligent and love your content and here is some feedback if you want use this as constructive critique to make even better videos. Make a script before you next video to organize your thoughts and don't repeat yourself.

  7. What do I do if I would like to predict the next values of a sine wave. The number of classes will not be 2. Is there anything that needs to be added?

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