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Author: JoelMarcey

advanced_source/cpp_extension.rst

@@ -1,6 +1,6 @@
 Custom C++ and CUDA Extensions
 ==============================
-**Author**: `Peter Goldsborough <https://goldsborough.me>`_
+**Author**: `Peter Goldsborough <https://www.goldsborough.me/>`_
 
 
 PyTorch provides a plethora of operations related to neural networks, arbitrary

beginner_source/deep_learning_60min_blitz.rst

@@ -1,6 +1,6 @@
 Deep Learning with PyTorch: A 60 Minute Blitz
 ---------------------------------------------
-**Author**: `Soumith Chintala <https://soumith.ch>`_
+**Author**: `Soumith Chintala <http://soumith.ch>`_
 
 Goal of this tutorial:
 

beginner_source/former_torchies_tutorial.rst

@@ -1,6 +1,6 @@
 PyTorch for Former Torch Users
 ------------------------------
-**Author**: `Soumith Chintala <https://soumith.ch>`_
+**Author**: `Soumith Chintala <http://soumith.ch>`_
 
 In this tutorial, you will learn the following:
 

beginner_source/nn_tutorial.py

@@ -2,7 +2,7 @@
 """
 What is `torch.nn` *really*?
 ============================
-by Jeremy Howard, `fast.ai <https://fast.ai>`_. Thanks to Rachel Thomas and Francisco Ingham.
+by Jeremy Howard, `fast.ai <https://www.fast.ai>`_. Thanks to Rachel Thomas and Francisco Ingham.
 """
 ###############################################################################
 # We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file,
@@ -29,13 +29,13 @@
 # MNIST data setup
 # ----------------
 #
-# We will use the classic `MNIST <https://deeplearning.net/data/mnist/>`_ dataset,
+# We will use the classic `MNIST <http://deeplearning.net/data/mnist/>`_ dataset,
 # which consists of black-and-white images of hand-drawn digits (between 0 and 9).
 #
 # We will use `pathlib <https://docs.python.org/3/library/pathlib.html>`_
 # for dealing with paths (part of the Python 3 standard library), and will
 # download the dataset using
-# `requests <https://docs.python-requests.org/en/master/>`_. We will only
+# `requests <http://docs.python-requests.org/en/master/>`_. We will only
 # import modules when we use them, so you can see exactly what's being
 # used at each point.
 
@@ -47,7 +47,7 @@
 
 PATH.mkdir(parents=True, exist_ok=True)
 
-URL = "https://deeplearning.net/data/mnist/"
+URL = "http://deeplearning.net/data/mnist/"
 FILENAME = "mnist.pkl.gz"
 
 if not (PATH / FILENAME).exists():
@@ -109,7 +109,7 @@
 # PyTorch signifies that the operation is performed in-place.)
 #
 # .. note:: We are initializing the weights here with
-#    `Xavier initialisation <https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
+#    `Xavier initialisation <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
 #    (by multiplying with 1/sqrt(n)).
 
 import math

beginner_source/pytorch_with_examples.rst

@@ -177,7 +177,7 @@ which will be optimized during learning.
 In TensorFlow, packages like
 `Keras <https://github.com/fchollet/keras>`__,
 `TensorFlow-Slim <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim>`__,
-and `TFLearn <https://tflearn.org/>`__ provide higher-level abstractions
+and `TFLearn <http://tflearn.org/>`__ provide higher-level abstractions
 over raw computational graphs that are useful for building neural
 networks.
 

intermediate_source/char_rnn_classification_tutorial.py

@@ -503,7 +503,7 @@ def predict(input_line, n_predictions=3):
 #     (-1.39) Polish
 #     (-3.51) Czech
 #
-# Run ``server.py`` and visit https://localhost:5533/Yourname to get JSON
+# Run ``server.py`` and visit http://localhost:5533/Yourname to get JSON
 # output of predictions.
 #
 

intermediate_source/dist_tuto.rst

@@ -494,7 +494,7 @@ field of high-performance computing. It allows to do point-to-point and
 collective communications and was the main inspiration for the API of
 ``torch.distributed``. Several implementations of MPI exist (e.g.
 `Open-MPI <https://www.open-mpi.org/>`__,
-`MVAPICH2 <https://mvapich.cse.ohio-state.edu/>`__, `Intel
+`MVAPICH2 <http://mvapich.cse.ohio-state.edu/>`__, `Intel
 MPI <https://software.intel.com/en-us/intel-mpi-library>`__) each
 optimized for different purposes. The advantage of using the MPI backend
 lies in MPI's wide availability - and high-level of optimization - on
@@ -606,7 +606,7 @@ that each process will open the file, write its information, and wait
 until everybody did so. After what all required information will be
 readily available to all processes. In order to avoid race conditions,
 the file system must support locking through
-`fcntl <https://man7.org/linux/man-pages/man2/fcntl.2.html>`__. Note that
+`fcntl <http://man7.org/linux/man-pages/man2/fcntl.2.html>`__. Note that
 you can specify ranks manually or let the processes figure it out by
 themselves. Be defining a unique ``groupname`` per job you can use the
 same file path for multiple jobs and safely avoid collision.

intermediate_source/seq2seq_translation_tutorial.py

@@ -521,7 +521,7 @@ def tensorsFromPair(pair):
 # each next input, instead of using the decoder's guess as the next input.
 # Using teacher forcing causes it to converge faster but `when the trained
 # network is exploited, it may exhibit
-# instability <https://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf>`__.
+# instability <http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf>`__.
 #
 # You can observe outputs of teacher-forced networks that read with
 # coherent grammar but wander far from the correct translation -