diff --git a/doc/index.rst b/doc/index.rst
index d29f1917adb136e8d07e102127c6e5fc8678bbaa..d24cdb6b97bf92ff40ea3aef216dfa15336b5e4c 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -38,6 +38,7 @@ Index of all packages
bob.bio.spear <bob/bob.bio.spear/doc/index.rst>
bob.bio.vein <bob/bob.bio.vein/doc/index.rst>
bob.bio.video <bob/bob.bio.video/doc/index.rst>
+ bob.devtools <bob/bob.devtools/doc/index.rst>
bob.extension <bob/bob.extension/doc/index.rst>
bob.fusion.base <bob/bob.fusion.base/doc/index.rst>
bob.io.base <bob/bob.io.base/doc/index.rst>
diff --git a/doc/readme_index.rst b/doc/readme_index.rst
index 7aa21c97fa3b40fb94b188812485162617c6ffd2..fe051273c259e235fe1f505b7cd831e950b24cd8 100644
--- a/doc/readme_index.rst
+++ b/doc/readme_index.rst
@@ -12,6 +12,7 @@ README of all Packages
bob.bio.spear <bob/bob.bio.spear/README.rst>
bob.bio.vein <bob/bob.bio.vein/README.rst>
bob.bio.video <bob/bob.bio.video/README.rst>
+ bob.devtools <bob/bob.devtools/README.rst>
bob.extension <bob/bob.extension/README.rst>
bob.fusion.base <bob/bob.fusion.base/README.rst>
bob.io.base <bob/bob.io.base/README.rst>
diff --git a/doc/tutorial.rst b/doc/tutorial.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f89b0c7235d3b5f8998fbf566e1b427098edb94c
--- /dev/null
+++ b/doc/tutorial.rst
@@ -0,0 +1,160 @@
+.. _bob.tutorial:
+
+********************************
+ Getting started with |project|
+********************************
+
+The following tutorial constitutes a suitable starting point to get to
+know how to use |project|'s packages and to learn its fundamental concepts.
+
+
+Multi-dimensional Arrays
+========================
+
+The fundamental data structure of |project| is a multi-dimensional array. In
+signal processing and machine learning, arrays are a suitable representation
+for many different types of digital signals such as images, audio data and
+extracted features. For multi-dimensional arrays, we rely on `NumPy`_.
+For an introduction and tutorials about NumPy ndarrays, just visit the `NumPy
+Reference`_ website.
+
+
+Digital signals as multi-dimensional arrays
+===========================================
+
+For |project|, we have decided to represent digital signals directly as
+:any:`numpy.ndarray` rather than having dedicated classes for each type of
+signals. This implies that some convention has been defined.
+
+Vectors and matrices
+--------------------
+
+A vector is represented as a 1D NumPy array, whereas a matrix is
+represented by a 2D array whose first dimension corresponds to the rows,
+and second dimension to the columns.
+
+.. code:: python
+
+ >>> import numpy
+ >>> A = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='uint8') # A is a matrix 2x3
+ >>> print(A)
+ [[1 2 3]
+ [4 5 6]]
+ >>> b = numpy.array([1, 2, 3], dtype='uint8') # b is a vector of length 3
+ >>> print(b)
+ [1 2 3]
+
+Images
+------
+
+**Grayscale** images are represented as 2D arrays, the first dimension
+being the height (number of rows) and the second dimension being the
+width (number of columns). For instance:
+
+.. code:: python
+
+ >>> img = numpy.ndarray((480,640), dtype='uint8')
+
+``img`` which is a 2D array can be seen as a gray-scale image of
+dimension 640 (width) by 480 (height). In addition, ``img`` can be seen
+as a matrix with 480 rows and 640 columns. This is the reason why we
+have decided that for images, the first dimension is the height and the
+second one the width, such that it matches the matrix convention as
+well.
+
+**Color** images are represented as 3D arrays, the first dimension being
+the number of color planes, the second dimension the height and the
+third the width. As an image is an array, this is the responsibility of
+the user to know in which color space the content is stored.
+:any:`bob.io.image` provides functions to convert Bob format images into
+Matplotlib_ and other formats and back:
+
+.. code:: python
+
+ >>> import bob.io.image
+ >>> colored_bob_format = numpy.ndarray((3,480,640), dtype='uint8')
+ >>> colored_matplotlib_format = bob.io.image.to_matplotlib(colored_bob_format)
+ >>> print(colored_matplotlib_format.shape)
+ [480 640 3]
+ >>> colored_bob_format = bob.io.image.to_bob(colored_matplotlib_format)
+ >>> print(colored_bob_format.shape)
+ [3 480 640]
+ >>> pillow_img = bob.io.image.bob_to_pillow(colored_bob_format)
+ >>> opencv_bgr = bob.io.image.bob_to_opencv(colored_bob_format)
+
+.. note::
+
+ In :ref:`bob.bio.face`, the images are assumed to be in range ``[0,255]``
+ irrespective of their data type.
+
+Videos
+------
+
+A video can be seen as a sequence of images over time. By convention, the first
+dimension is for the frame indices (time index), whereas the remaining ones are
+related to the corresponding image frame. The videos have the shape of
+``(N,C,H,W)``, where ``N`` is the number of frames, ``H`` the height, ``W`` the
+width and ``C`` the number of color planes.
+
+
+Input and output
+================
+
+:ref:`bob.io.base` provides two generic functions :any:`bob.io.base.load` and
+:any:`bob.io.base.save` to load and save data of various types, based on the
+filename extension. For example, to load a ``.jpg`` image, simply call:
+
+.. code:: python
+
+ >>> import bob.io.base
+ >>> img = bob.io.base.load("myimg.jpg")
+
+`HDF5`_ format, through h5py_, and images, through imageio_, are supported.
+For loading videos, use imageio-ffmpeg_ directly.
+
+
+Machine learning
+================
+
+:ref:`bob.learn.em` provides implementation of the following methods:
+
+ - K-Means clustering
+ - Gaussian Mixture Modeling (GMM)
+ - Joint Factor Analysis (JFA)
+ - Inter-Session Variability (ISV)
+ - Total Variability (TV, also known as i-vector)
+ - Probabilistic Linear Discriminant Analysis (PLDA, also known as i-vector)
+
+All implementations use dask_ to parallelize the training computation.
+
+Database interfaces
+===================
+
+Bob provides an API on top of CSV files to easily query databases.
+A generic implementation is provided in :ref:`bob.pipelines` but packages
+such as :ref:`bob.bio.base` and :ref:`bob.pad.base` provide their own implementations.
+
+Performance evaluation
+======================
+
+Methods in the :ref:`bob.measure` module can be used evaluate error for
+multi-class or binary classification problems. Several evaluation
+techniques such as:
+
+ - Root Mean Squared Error (RMSE)
+ - F-score
+ - Precision and Recall
+ - False Positive Rate (FPR)
+ - False Negative Rate (FNR)
+ - Equal Error Rates (EER)
+
+can be computed. Moreover, functionality for plotting
+
+ - ROC
+ - DET
+ - CMC
+ - EPC
+
+curves are described in more detail in the :ref:`bob.measure`.
+
+.. include:: links.rst