Deep Learning

In this repository, I've coded up a series of examples from "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Geron.

While Aurélien provides a series of notebooks: https://github.com/ageron/handson-ml2 here I have put together a series examples written in Python to show how these can be put together and run from the command line.

From Chapter 9 on TensorFlow

ch9_simple_graph.py: shows the basic mechanics of the TensorFlow framework

ch9_linear_regression.py: shows how TensorFlow can be configured to perform linear regression

ch9_gradient_descent.py: shows how you can train the network using the full set of data

ch9_mini_batch_gradient_descent.py: shows how you can train the network using mini-batches

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
README.md		README.md
assignment.py		assignment.py
assignment_example.py		assignment_example.py
ch10_perceptron.py		ch10_perceptron.py
ch10_tensorflow_mlp.py		ch10_tensorflow_mlp.py
ch13_convolutional.py		ch13_convolutional.py
ch9_gradient_descent.py		ch9_gradient_descent.py
ch9_linear_regression.py		ch9_linear_regression.py
ch9_logging.py		ch9_logging.py
ch9_logging_with_scope.py		ch9_logging_with_scope.py
ch9_mini_batch_gradient_descent.py		ch9_mini_batch_gradient_descent.py
ch9_moons_exercise.py		ch9_moons_exercise.py
ch9_restoring_models.py		ch9_restoring_models.py
ch9_reusing_relus.py		ch9_reusing_relus.py
ch9_saving_models.py		ch9_saving_models.py
ch9_simple_graph.py		ch9_simple_graph.py
example.py		example.py
fully_connected.py		fully_connected.py
helpers.py		helpers.py
imdb.py		imdb.py
load_forest_data.py		load_forest_data.py
mnist.py		mnist.py
requirements.txt		requirements.txt

leifos/deeplearning

Folders and files

Latest commit

History

Repository files navigation

Deep Learning

From Chapter 9 on TensorFlow

About

Resources

Stars

Watchers

Forks

Languages