diff --git a/doc/DeepSpeech.md b/doc/DeepSpeech.md
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-
-# Introduction
-
-In this notebook we will reproduce the results of
-[Deep Speech: Scaling up end-to-end speech recognition](http://arxiv.org/abs/1412.5567).
-The core of the system is a bidirectional recurrent neural network (BRNN)
-trained to ingest speech spectrograms and generate English text transcriptions.
-
-Let a single utterance and label be sampled from a training set
-.
-Each utterance, is a time-series of length
-where every time-slice is a vector of audio features,
- where .
-We use MFCC as our features; so denotes the -th MFCC feature
-in the audio frame at time . The goal of our BRNN is to convert an input
-sequence into a sequence of character probabilities for the transcription
-, with ,
-where .
-(The significance of will be explained below.)
-
-Our BRNN model is composed of layers of hidden units.
-For an input , the hidden units at layer are denoted with the
-convention that is the input. The first three layers are not recurrent.
-For the first layer, at each time , the output depends on the MFCC frame
- along with a context of frames on each side.
-(We typically use for our experiments.)
-The remaining non-recurrent layers operate on independent data for each time step.
-Thus, for each time , the first layers are computed by:
-
-
-
-where is a clipped rectified-linear (ReLu)
-activation function and , are the weight matrix and bias
-parameters for layer . The fourth layer is a bidirectional recurrent
-layer[[1](http://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf)].
-This layer includes two sets of hidden units: a set with forward recurrence,
-, and a set with backward recurrence :
-
-
-
-
-Note that must be computed sequentially from to
-for the -th utterance, while the units must be computed
-sequentially in reverse from to .
-
-The fifth (non-recurrent) layer takes both the forward and backward units as inputs
-
-
-
-where . The output layer are standard logits that
-correspond to the predicted character probabilities for each time slice and
-character in the alphabet:
-
-
-
-Here denotes the -th bias and the -th
-element of the matrix product.
-
-Once we have computed a prediction for , we compute the CTC loss
-[[2]](http://www.cs.toronto.edu/~graves/preprint.pdf)
-to measure the error in prediction. During training, we can evaluate the gradient
- with respect to the network outputs given the
-ground-truth character sequence . From this point, computing the gradient
-with respect to all of the model parameters may be done via back-propagation
-through the rest of the network. We use the Adam method for training
-[[3](http://arxiv.org/abs/1412.6980)].
-
-The complete BRNN model is illustrated in the figure below.
-
-![DeepSpeech BRNN](../images/rnn_fig-624x548.png)
diff --git a/doc/DeepSpeech.raw b/doc/DeepSpeech.rst
similarity index 100%
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rename to doc/DeepSpeech.rst
diff --git a/doc/Geometry.md b/doc/Geometry.md
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--- a/doc/Geometry.md
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-# Geometric Constants
-
-This is about several constants related to the geometry of the network.
-
-## n_steps
-The network views each speech sample as a sequence of time-slices of
-length . As the speech samples vary in length, we know that
-need not equal for . For each batch, BRNN in TensorFlow needs
-to know `n_steps` which is the maximum for the batch.
-
-## n_input
-Each of the at maximum `n_steps` vectors is a vector of MFCC features of a
-time-slice of the speech sample. We will make the number of MFCC features
-dependent upon the sample rate of the data set. Generically, if the sample rate
-is 8kHz we use 13 features. If the sample rate is 16kHz we use 26 features...
-We capture the dimension of these vectors, equivalently the number of MFCC
-features, in the variable `n_input`
-
-## n_context
-As previously mentioned, the BRNN is not simply fed the MFCC features of a given
-time-slice. It is fed, in addition, a context of frames on
-either side of the frame in question. The number of frames in this context is
-captured in the variable `n_context`
-
-Next we will introduce constants that specify the geometry of some of the
-non-recurrent layers of the network. We do this by simply specifying the number
-of units in each of the layers
-
-## n_hidden_1, n_hidden_2, n_hidden_5
-`n_hidden_1` is the number of units in the first layer, `n_hidden_2` the number
-of units in the second, and `n_hidden_5` the number in the fifth. We haven't
-forgotten about the third or sixth layer. We will define their unit count below.
-
-A LSTM BRNN consists of a pair of LSTM RNN's.
-One LSTM RNN that works "forward in time"
-
-
-
-and a second LSTM RNN that works "backwards in time"
-
-
-
-The dimension of the cell state, the upper line connecting subsequent LSTM units,
-is independent of the input dimension and the same for both the forward and
-backward LSTM RNN.
-
-## n_cell_dim
-Hence, we are free to choose the dimension of this cell state independent of the
-input dimension. We capture the cell state dimension in the variable `n_cell_dim`.
-
-## n_hidden_3
-The number of units in the third layer, which feeds in to the LSTM, is
-determined by `n_cell_dim` as follows
-```python
-n_hidden_3 = 2 * n_cell_dim
-```
-
-## n_character
-The variable `n_character` will hold the number of characters in the target
-language plus one, for the .
-For English it is the cardinality of the set
-
-we referred to earlier.
-
-## n_hidden_6
-The number of units in the sixth layer is determined by `n_character` as follows
-```python
-n_hidden_6 = n_character
-```
diff --git a/doc/Geometry.raw b/doc/Geometry.rst
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diff --git a/doc/ParallelOptimization.md b/doc/ParallelOptimization.md
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-# Parallel Optimization
-
-This is how we implement optimization of the DeepSpeech model across GPU's on a
-single host. Parallel optimization can take on various forms. For example
-one can use asynchronous updates of the model, synchronous updates of the model,
-or some combination of the two.
-
-## Asynchronous Parallel Optimization
-
-In asynchronous parallel optimization, for example, one places the model
-initially in CPU memory. Then each of the GPU's obtains a mini-batch of data
-along with the current model parameters. Using this mini-batch each GPU then
-computes the gradients for all model parameters and sends these gradients back
-to the CPU when the GPU is done with its mini-batch. The CPU then asynchronously
-updates the model parameters whenever it recieves a set of gradients from a GPU.
-
-Asynchronous parallel optimization has several advantages and several
-disadvantages. One large advantage is throughput. No GPU will every be waiting
-idle. When a GPU is done processing a mini-batch, it can immediately obtain the
-next mini-batch to process. It never has to wait on other GPU's to finish their
-mini-batch. However, this means that the model updates will also be asynchronous
-which can have problems.
-
-For example, one may have model parameters on the CPU and send mini-batch
- to GPU 1 and send mini-batch to GPU 2. As processing is asynchronous,
-GPU 2 may finish before GPU 1 and thus update the CPU's model parameters
-with its gradients , where the subscript identifies the
-mini-batch and the argument the location at which the gradient was evaluated.
-This results in the new model parameters
-
-
-
-Next GPU 1 could finish with its mini-batch and update the parameters to
-
-
-
-The problem with this is that is evaluated at and not
-. Hence, the direction of the gradient
-is slightly incorrect as it is evaluated at the wrong location. This can be
-counteracted through synchronous updates of model, but this is also problematic.
-
-## Synchronous Optimization
-
-Synchronous optimization solves the problem we saw above. In synchronous
-optimization, one places the model initially in CPU memory. Then one of the
-GPU's is given a mini-batch of data along with the current model parameters.
-Using the mini-batch the GPU computes the gradients for all model parameters and
-sends the gradients back to the CPU. The CPU then updates the model parameters
-and starts the process of sending out the next mini-batch.
-
-As on can readily see, synchronous optimization does not have the problem we
-found in the last section, that of incorrect gradients. However, synchronous
-optimization can only make use of a single GPU at a time. So, when we have a
-multi-GPU setup, , all but one of the GPU's will remain idle, which is
-unacceptable. However, there is a third alternative which is combines the
-advantages of asynchronous and synchronous optimization.
-
-## Hybrid Parallel Optimization
-
-Hybrid parallel optimization combines most of the benifits of asynchronous and
-synchronous optimization. It allows for multiple GPU's to be used, but does not
-suffer from the incorrect gradient problem exhibited by asynchronous
-optimization.
-
-In hybrid parallel optimization one places the model initially in CPU memory.
-Then, as in asynchronous optimization, each of the GPU'S obtains a
-mini-batch of data along with the current model parameters. Using the mini-batch
-each of the GPU's then computes the gradients for all model parameters and sends
-these gradients back to the CPU. Now, in contrast to asynchronous optimization,
-the CPU waits until each GPU is finished with its mini-batch then takes the mean
-of all the gradients from the GPU's and updates the model with this mean
-gradient.
-
-
-
-Hybrid parallel optimization has several advantages and few disadvantages. As in
-asynchronous parallel optimization, hybrid parallel optimization allows for one
-to use multiple GPU's in parallel. Furthermore, unlike asynchronous parallel
-optimization, the incorrect gradient problem is not present here. In fact,
-hybrid parallel optimization performs as if one is working with a single
-mini-batch which is times the size of a mini-batch handled by a single GPU.
-Hoewever, hybrid parallel optimization is not perfect. If one GPU is slower than
-all the others in completing its mini-batch, all other GPU's will have to sit
-idle until this straggler finishes with its mini-batch. This hurts throughput.
-But, if all GPU'S are of the same make and model, this problem should be
-minimized.
-
-So, relatively speaking, hybrid parallel optimization seems the have more
-advantages and fewer disadvantages as compared to both asynchronous and
-synchronous optimization. So, we will, for our work, use this hybrid model.
-
-## Adam Optimization
-
-In constrast to
-[Deep Speech: Scaling up end-to-end speech recognition](http://arxiv.org/abs/1412.5567),
-in which
-[Nesterov’s Accelerated Gradient Descent](www.cs.toronto.edu/~fritz/absps/momentum.pdf)
-was used, we will use the Adam method for optimization
-[[3](http://arxiv.org/abs/1412.6980)],
-because, generally, it requires less fine-tuning.
diff --git a/doc/ParallelOptimization.raw b/doc/ParallelOptimization.rst
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diff --git a/doc/README.md b/doc/README.md
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-This directory contains further documentation.
-
-Some of the files contain `TeX` code and therefore need to be generated. So if you want to edit a markdown file that has a `.raw` counterpart, please edit the `raw` one and call `doc/gendocs.sh` from the project root. This will (re)generate the corresponding `.md` file. Please make sure to always commit both files.
-
-Note that you have to follow [this](https://github.com/leegao/readme2tex#installation) installation instructions first.
diff --git a/doc/gendocs.sh b/doc/gendocs.sh
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-#!/bin/sh
-
-set -xe
-
-python -m readme2tex --svgdir doc/svgs/ --username mozilla --project DeepSpeech --output doc/DeepSpeech.md doc/DeepSpeech.raw
-python -m readme2tex --svgdir doc/svgs/ --username mozilla --project DeepSpeech --output doc/Geometry.md doc/Geometry.raw
-python -m readme2tex --svgdir doc/svgs/ --username mozilla --project DeepSpeech --output doc/ParallelOptimization.md doc/ParallelOptimization.raw
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