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Tutorials
=========


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    <div class="sphx-glr-thumbcontainer" tooltip="This notebook describes dense layer or fully connected layer using tensorflow.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_dense_keras_thumb.png
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  :ref:`sphx_glr_auto_examples_dense_keras.py`

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      <div class="sphx-glr-thumbnail-title">understanding Dense layer in Keras</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The purpose of this notebook is to illustrate how to build an LSTM from scratch in numpy.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_lstm_numpy_scratch_thumb.png
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  :ref:`sphx_glr_auto_examples_lstm_numpy_scratch.py`

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      <div class="sphx-glr-thumbnail-title">Implementing LSTM in numpy from scratch</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The purpose of this notebook to determine the input and output shapes of LSTM in keras/tensorflow. It also shows how the output changes when we use different options such as return_sequences and return_state arguments in LSTM/RNN layers of tensorflow/keras.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_input_output_lstm_thumb.png
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  :ref:`sphx_glr_auto_examples_input_output_lstm.py`

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      <div class="sphx-glr-thumbnail-title">understanding Input/output of LSTM</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="ANN in numpy">

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  .. image:: /auto_examples/images/thumb/sphx_glr_ann_thumb.png
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  :ref:`sphx_glr_auto_examples_ann.py`

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      <div class="sphx-glr-thumbnail-title">ANN in numpy</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="understanding Dense layer in Keras">

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  .. image:: /auto_examples/images/thumb/sphx_glr_time_distributed_keras_thumb.png
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  :ref:`sphx_glr_auto_examples_time_distributed_keras.py`

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      <div class="sphx-glr-thumbnail-title">understanding Dense layer in Keras</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to prepare data for time series prediction especially for deep learning models/algorithms like LSTM/RNN.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_preparing_data_for_ts_prediction_thumb.png
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  :ref:`sphx_glr_auto_examples_preparing_data_for_ts_prediction.py`

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      <div class="sphx-glr-thumbnail-title">Data Preparation for Time Series Prediction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The purpose of this notebook is to illustrate how to build an LSTM from scratch in Tensorflow. Although the Tensorflow has implementation of LSTM in Keras. But since it comes with a lot of implementation options, reading the code of Tensorflow for LSTM can be confusing at the start. Therefore here is vanilla implementation of LSTM in Tensorflow. It has been shown that the results of this vanilla LSTM are full reproducible with Keras&#x27;LSTM. This shows that the simple implementation of LSTM in Tensorflow just has four equations and a for loop through time.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_lstm_tensorflow_scratch_thumb.png
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  :ref:`sphx_glr_auto_examples_lstm_tensorflow_scratch.py`

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      <div class="sphx-glr-thumbnail-title">Implementing LSTM in tensorflow from scratch</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="utility functions">

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  .. image:: /auto_examples/images/thumb/sphx_glr_utils_thumb.png
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  :ref:`sphx_glr_auto_examples_utils.py`

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      <div class="sphx-glr-thumbnail-title">utility functions</div>
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.. toctree::
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   /auto_examples/dense_keras
   /auto_examples/lstm_numpy_scratch
   /auto_examples/input_output_lstm
   /auto_examples/ann
   /auto_examples/time_distributed_keras
   /auto_examples/preparing_data_for_ts_prediction
   /auto_examples/lstm_tensorflow_scratch
   /auto_examples/utils


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  .. container:: sphx-glr-footer sphx-glr-footer-gallery

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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