How To Install python-autograd-doc on Fedora 36
In this tutorial we learn how to install python-autograd-doc in Fedora 36. python-autograd-doc is Efficiently computes derivatives of numpy code
Introduction
In this tutorial we learn how to install python-autograd-doc on Fedora 36.
What is python-autograd-doc
Documentation for python-autograd.
We can use yum or dnf to install python-autograd-doc on Fedora 36. In this tutorial we discuss both methods but you only need to choose one of method to install python-autograd-doc.
Install python-autograd-doc on Fedora 36 Using dnf
Update yum database with dnf using the following command.
sudo dnf makecache --refresh
After updating yum database, We can install python-autograd-doc using dnf by running the following command:
sudo dnf -y install python-autograd-doc
Install python-autograd-doc on Fedora 36 Using yum
Update yum database with yum using the following command.
sudo yum makecache --refresh
After updating yum database, We can install python-autograd-doc using yum by running the following command:
sudo yum -y install python-autograd-doc
How To Uninstall python-autograd-doc on Fedora 36
To uninstall only the python-autograd-doc package we can use the following command:
sudo dnf remove python-autograd-doc
python-autograd-doc Package Contents on Fedora 36
/usr/share/doc/python-autograd-doc
/usr/share/doc/python-autograd-doc/examples
/usr/share/doc/python-autograd-doc/examples/__init__.py
/usr/share/doc/python-autograd-doc/examples/bayesian_neural_net.png
/usr/share/doc/python-autograd-doc/examples/bayesian_neural_net.py
/usr/share/doc/python-autograd-doc/examples/bayesian_optimization.py
/usr/share/doc/python-autograd-doc/examples/black_box_svi.py
/usr/share/doc/python-autograd-doc/examples/convnet.py
/usr/share/doc/python-autograd-doc/examples/data.py
/usr/share/doc/python-autograd-doc/examples/data_mnist.py
/usr/share/doc/python-autograd-doc/examples/deep_gaussian_process.py
/usr/share/doc/python-autograd-doc/examples/define_gradient.py
/usr/share/doc/python-autograd-doc/examples/dot_graph.py
/usr/share/doc/python-autograd-doc/examples/fixed_points.py
/usr/share/doc/python-autograd-doc/examples/fluidsim
/usr/share/doc/python-autograd-doc/examples/fluidsim/animated.gif
/usr/share/doc/python-autograd-doc/examples/fluidsim/fluidsim.py
/usr/share/doc/python-autograd-doc/examples/fluidsim/init_smoke.png
/usr/share/doc/python-autograd-doc/examples/fluidsim/peace.png
/usr/share/doc/python-autograd-doc/examples/fluidsim/skull.png
/usr/share/doc/python-autograd-doc/examples/fluidsim/surprise.gif
/usr/share/doc/python-autograd-doc/examples/fluidsim/wing.png
/usr/share/doc/python-autograd-doc/examples/fluidsim/wing.py
/usr/share/doc/python-autograd-doc/examples/gaussian_process.png
/usr/share/doc/python-autograd-doc/examples/gaussian_process.py
/usr/share/doc/python-autograd-doc/examples/generative_adversarial_net.py
/usr/share/doc/python-autograd-doc/examples/gmm.png
/usr/share/doc/python-autograd-doc/examples/gmm.py
/usr/share/doc/python-autograd-doc/examples/gplvm.png
/usr/share/doc/python-autograd-doc/examples/gplvm.py
/usr/share/doc/python-autograd-doc/examples/graph.pdf
/usr/share/doc/python-autograd-doc/examples/hmm_em.py
/usr/share/doc/python-autograd-doc/examples/ica.py
/usr/share/doc/python-autograd-doc/examples/logistic_regression.py
/usr/share/doc/python-autograd-doc/examples/lstm.py
/usr/share/doc/python-autograd-doc/examples/mixture_variational_inference.py
/usr/share/doc/python-autograd-doc/examples/natural_gradient_black_box_svi.py
/usr/share/doc/python-autograd-doc/examples/negative_binomial_maxlike.py
/usr/share/doc/python-autograd-doc/examples/neural_net.py
/usr/share/doc/python-autograd-doc/examples/neural_net_regression.py
/usr/share/doc/python-autograd-doc/examples/ode_net.py
/usr/share/doc/python-autograd-doc/examples/ode_net_demo.png
/usr/share/doc/python-autograd-doc/examples/print_trace.py
/usr/share/doc/python-autograd-doc/examples/rkhs.py
/usr/share/doc/python-autograd-doc/examples/rnn.py
/usr/share/doc/python-autograd-doc/examples/rosenbrock.py
/usr/share/doc/python-autograd-doc/examples/sinusoid.png
/usr/share/doc/python-autograd-doc/examples/sinusoid.py
/usr/share/doc/python-autograd-doc/examples/sinusoid_taylor.png
/usr/share/doc/python-autograd-doc/examples/tanh.png
/usr/share/doc/python-autograd-doc/examples/tanh.py
/usr/share/doc/python-autograd-doc/examples/vae_samples.png
/usr/share/doc/python-autograd-doc/examples/variational_autoencoder.py
/usr/share/licenses/python-autograd-doc
/usr/share/licenses/python-autograd-doc/license.txt
References
Summary
In this tutorial we learn how to install python-autograd-doc on Fedora 36 using yum and [dnf]((/fedora/36/dnf/).
