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Fix broken links in cpp_frontend.rst #3245

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Jan 23, 2025
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Fix broken links in cpp_frontend.rst #3245

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Update cpp_frontend.rst
sekyondaMeta Jan 22, 2025
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Merge branch 'main' into sekyondaMeta-cpp_frontendLinks
sekyondaMeta Jan 23, 2025
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4 changes: 2 additions & 2 deletions advanced_source/cpp_frontend.rst
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Expand Up @@ -57,7 +57,7 @@ the right tool for the job. Examples for such environments include:
Multiprocessing is an alternative, but not as scalable and has significant
shortcomings. C++ has no such constraints and threads are easy to use and
create. Models requiring heavy parallelization, like those used in `Deep
Neuroevolution <https://eng.uber.com/deep-neuroevolution/>`_, can benefit from
Neuroevolution <https://www.uber.com/blog/deep-neuroevolution/>`_, can benefit from
this.
- **Existing C++ Codebases**: You may be the owner of an existing C++
application doing anything from serving web pages in a backend server to
Expand Down Expand Up @@ -662,7 +662,7 @@ Defining the DCGAN Modules
We now have the necessary background and introduction to define the modules for
the machine learning task we want to solve in this post. To recap: our task is
to generate images of digits from the `MNIST dataset
<http://yann.lecun.com/exdb/mnist/>`_. We want to use a `generative adversarial
<https://huggingface.co/datasets/ylecun/mnist>`_. We want to use a `generative adversarial
network (GAN)
<https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf>`_ to solve
this task. In particular, we'll use a `DCGAN architecture
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