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‎doc/pub/week47/html/._week47-bs000.html‎

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('TensorFlow examples', 2, None, 'tensorflow-examples'),
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('What is a recurrent NN?', 2, None, 'what-is-a-recurrent-nn'),
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('Why RNNs?', 2, None, 'why-rnns'),
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('More whys', 2, None, 'more-whys'),
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('RNNs in more detail', 2, None, 'rnns-in-more-detail'),
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('RNNs in more detail, part 2',
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2,
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2,
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None,
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'rnns-in-more-detail-part-7'),
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('RNN Forward Pass Equations',
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2,
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None,
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'rnn-forward-pass-equations'),
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('Unrolled RNN in Time', 2, None, 'unrolled-rnn-in-time'),
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('Example Task: Character-level RNN Classification',
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2,
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None,
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'example-task-character-level-rnn-classification'),
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('PyTorch: Defining a Simple RNN, using Tensorflow',
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2,
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None,
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'pytorch-defining-a-simple-rnn-using-tensorflow'),
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('Similar example using PyTorch',
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2,
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None,
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'similar-example-using-pytorch'),
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('Backpropagation Through Time (BPTT) and Gradients',
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2,
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None,
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'backpropagation-through-time-bptt-and-gradients'),
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('Truncated BPTT and Gradient Clipping',
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2,
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None,
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'truncated-bptt-and-gradient-clipping'),
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('Applications of Simple RNNs',
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2,
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None,
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'applications-of-simple-rnns'),
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('Sequence Modeling Tasks', 2, None, 'sequence-modeling-tasks'),
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('Other Sequence Applications',
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2,
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None,
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'other-sequence-applications'),
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('Training and Practical Tips',
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2,
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None,
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'training-and-practical-tips'),
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('Limitations and Considerations',
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2,
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None,
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'limitations-and-considerations'),
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('PyTorch RNN Time Series Example',
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2,
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None,
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'pytorch-rnn-time-series-example'),
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('Tensorflow (Keras) RNN Time Series Example',
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2,
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None,
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'tensorflow-keras-rnn-time-series-example'),
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('The mathematics of RNNs, the basic architecture',
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<!-- navigation toc: --> <li><a href="._week47-bs003.html#tensorflow-examples" style="font-size: 80%;">TensorFlow examples</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs004.html#what-is-a-recurrent-nn" style="font-size: 80%;">What is a recurrent NN?</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs005.html#why-rnns" style="font-size: 80%;">Why RNNs?</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#rnns-in-more-detail" style="font-size: 80%;">RNNs in more detail</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#rnns-in-more-detail-part-2" style="font-size: 80%;">RNNs in more detail, part 2</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs008.html#rnns-in-more-detail-part-3" style="font-size: 80%;">RNNs in more detail, part 3</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs009.html#rnns-in-more-detail-part-4" style="font-size: 80%;">RNNs in more detail, part 4</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs010.html#rnns-in-more-detail-part-5" style="font-size: 80%;">RNNs in more detail, part 5</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs011.html#rnns-in-more-detail-part-6" style="font-size: 80%;">RNNs in more detail, part 6</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs012.html#rnns-in-more-detail-part-7" style="font-size: 80%;">RNNs in more detail, part 7</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#the-mathematics-of-rnns-the-basic-architecture" style="font-size: 80%;">The mathematics of RNNs, the basic architecture</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#gating-mechanism-long-short-term-memory-lstm" style="font-size: 80%;">Gating mechanism: Long Short Term Memory (LSTM)</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs015.html#implementing-a-memory-cell-in-a-neural-network" style="font-size: 80%;">Implementing a memory cell in a neural network</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs018.html#lstm-details" style="font-size: 80%;">LSTM details</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs017.html#basic-layout-all-figures-from-raschka-et-al" style="font-size: 80%;">Basic layout (All figures from Raschka <em>et al.,</em>)</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs018.html#lstm-details" style="font-size: 80%;">LSTM details</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs019.html#comparing-with-a-standard-rnn" style="font-size: 80%;">Comparing with a standard RNN</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs020.html#lstm-details-i" style="font-size: 80%;">LSTM details I</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs021.html#lstm-details-ii" style="font-size: 80%;">LSTM details II</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs022.html#lstm-details-iii" style="font-size: 80%;">LSTM details III</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs023.html#forget-gate" style="font-size: 80%;">Forget gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs024.html#the-forget-gate" style="font-size: 80%;">The forget gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs029.html#basic-layout" style="font-size: 80%;">Basic layout</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs026.html#input-gate" style="font-size: 80%;">Input gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs027.html#short-summary" style="font-size: 80%;">Short summary</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs028.html#forget-and-input" style="font-size: 80%;">Forget and input</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs029.html#basic-layout" style="font-size: 80%;">Basic layout</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs030.html#output-gate" style="font-size: 80%;">Output gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs031.html#summary-of-lstm" style="font-size: 80%;">Summary of LSTM</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs032.html#lstm-implementation-using-tensorflow" style="font-size: 80%;">LSTM implementation using TensorFlow</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs033.html#and-the-corresponding-one-with-pytorch" style="font-size: 80%;">And the corresponding one with PyTorch</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs034.html#dynamical-ordinary-differential-equation" style="font-size: 80%;">Dynamical ordinary differential equation</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs035.html#the-runge-kutta-4-code" style="font-size: 80%;">The Runge-Kutta-4 code</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs036.html#using-the-above-data-to-train-an-rnn" style="font-size: 80%;">Using the above data to train an RNN</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#more-whys" style="font-size: 80%;">More whys</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#rnns-in-more-detail" style="font-size: 80%;">RNNs in more detail</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs008.html#rnns-in-more-detail-part-2" style="font-size: 80%;">RNNs in more detail, part 2</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs009.html#rnns-in-more-detail-part-3" style="font-size: 80%;">RNNs in more detail, part 3</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs010.html#rnns-in-more-detail-part-4" style="font-size: 80%;">RNNs in more detail, part 4</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs011.html#rnns-in-more-detail-part-5" style="font-size: 80%;">RNNs in more detail, part 5</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs012.html#rnns-in-more-detail-part-6" style="font-size: 80%;">RNNs in more detail, part 6</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#rnns-in-more-detail-part-7" style="font-size: 80%;">RNNs in more detail, part 7</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#rnn-forward-pass-equations" style="font-size: 80%;">RNN Forward Pass Equations</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs015.html#unrolled-rnn-in-time" style="font-size: 80%;">Unrolled RNN in Time</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs016.html#example-task-character-level-rnn-classification" style="font-size: 80%;">Example Task: Character-level RNN Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs017.html#pytorch-defining-a-simple-rnn-using-tensorflow" style="font-size: 80%;">PyTorch: Defining a Simple RNN, using Tensorflow</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs018.html#similar-example-using-pytorch" style="font-size: 80%;">Similar example using PyTorch</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs019.html#backpropagation-through-time-bptt-and-gradients" style="font-size: 80%;">Backpropagation Through Time (BPTT) and Gradients</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs020.html#truncated-bptt-and-gradient-clipping" style="font-size: 80%;">Truncated BPTT and Gradient Clipping</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs021.html#applications-of-simple-rnns" style="font-size: 80%;">Applications of Simple RNNs</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs022.html#sequence-modeling-tasks" style="font-size: 80%;">Sequence Modeling Tasks</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs023.html#other-sequence-applications" style="font-size: 80%;">Other Sequence Applications</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs024.html#training-and-practical-tips" style="font-size: 80%;">Training and Practical Tips</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs025.html#limitations-and-considerations" style="font-size: 80%;">Limitations and Considerations</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs026.html#pytorch-rnn-time-series-example" style="font-size: 80%;">PyTorch RNN Time Series Example</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs027.html#tensorflow-keras-rnn-time-series-example" style="font-size: 80%;">Tensorflow (Keras) RNN Time Series Example</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs028.html#the-mathematics-of-rnns-the-basic-architecture" style="font-size: 80%;">The mathematics of RNNs, the basic architecture</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs029.html#gating-mechanism-long-short-term-memory-lstm" style="font-size: 80%;">Gating mechanism: Long Short Term Memory (LSTM)</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs030.html#implementing-a-memory-cell-in-a-neural-network" style="font-size: 80%;">Implementing a memory cell in a neural network</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs033.html#lstm-details" style="font-size: 80%;">LSTM details</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs032.html#basic-layout-all-figures-from-raschka-et-al" style="font-size: 80%;">Basic layout (All figures from Raschka <em>et al.,</em>)</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs033.html#lstm-details" style="font-size: 80%;">LSTM details</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs034.html#comparing-with-a-standard-rnn" style="font-size: 80%;">Comparing with a standard RNN</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs035.html#lstm-details-i" style="font-size: 80%;">LSTM details I</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs036.html#lstm-details-ii" style="font-size: 80%;">LSTM details II</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs037.html#lstm-details-iii" style="font-size: 80%;">LSTM details III</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs038.html#forget-gate" style="font-size: 80%;">Forget gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-forget-gate" style="font-size: 80%;">The forget gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs044.html#basic-layout" style="font-size: 80%;">Basic layout</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs041.html#input-gate" style="font-size: 80%;">Input gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs042.html#short-summary" style="font-size: 80%;">Short summary</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs043.html#forget-and-input" style="font-size: 80%;">Forget and input</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs044.html#basic-layout" style="font-size: 80%;">Basic layout</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs045.html#output-gate" style="font-size: 80%;">Output gate</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-lstm" style="font-size: 80%;">Summary of LSTM</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs047.html#lstm-implementation-using-tensorflow" style="font-size: 80%;">LSTM implementation using TensorFlow</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs048.html#and-the-corresponding-one-with-pytorch" style="font-size: 80%;">And the corresponding one with PyTorch</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs049.html#dynamical-ordinary-differential-equation" style="font-size: 80%;">Dynamical ordinary differential equation</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs050.html#the-runge-kutta-4-code" style="font-size: 80%;">The Runge-Kutta-4 code</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs051.html#using-the-above-data-to-train-an-rnn" style="font-size: 80%;">Using the above data to train an RNN</a></li>
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<li><a href="._week47-bs008.html">9</a></li>
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