Python for Machine Learning

Python notebooks and experiments

Python Tutorial

python tutorial, staging for deep/machine learning course

1️⃣ Lesson 1 2️⃣ Lesson 2 3️⃣ Lesson 3 4️⃣ Lesson 4 5️⃣ lesson 5

Deep Learning

Originally designed to teach Alex python, thank you for your constant learning enthusiasm

Also created for coderbunker deep learning talk sessions

This course is the basic deep learning course that follows closely on Jeremy Howard's fantasic /free /life-changing fast.ai course.

The most of notebooks are just trails we left behind passing on their awesomeness.

Checklist before we start and a reading list

Experiments

The coord conv from the uber paper

Ray's toolbox for deep learning

Fast KMeans by batch with GPU, how to train a 60 minutes kmeans in 3 seconds, with example here

A PyTorch training wrapper to simplify tracking

Environment Installation

Follow the installation instructions

Run the jupyter notebook on anaconda3 environment

Usual pre-requisites for the learning.

python 3.6

numpy == '1.14.3'
pandas == '0.23.0'
tensorflow == '1.8.0'
keras == '2.2.0'

Other versions of above library will probably work.

Assuming your anaconda3 is at ~/anaconda3/

If you don't have any of these, try the following format in the command line:

~/anaconda3/bin/pip install keras==2.2.0

If you are on Mac:

~/anaconda3/bin/pip install torch torchvision

To install PyTorch. For other system, You'll have to visit their homepage to copy/paste the right command to install.

Libs

In some lines of code you might see

from forgebox.imports import *

You can do

pip install forgebox

Contact Us

If you want to be a contributor:

mail: b2ray2c@gmail.com

wechat: 417848506 remark:"python4ml"

Name		Name	Last commit message	Last commit date
Latest commit History 433 Commits
.idea		.idea
coursera		coursera
docs		docs
experiments		experiments
openimage		openimage
papers		papers
.gitignore		.gitignore
1.1_2_variables_functions_and_loops.ipynb		1.1_2_variables_functions_and_loops.ipynb
1.1_lesson1.ipynb		1.1_lesson1.ipynb
1.2_lesson2.ipynb		1.2_lesson2.ipynb
1.3_lesson3.ipynb		1.3_lesson3.ipynb
1.4_lesson4.ipynb		1.4_lesson4.ipynb
1.5.1_oop_example.ipynb		1.5.1_oop_example.ipynb
1.5_lesson5.ipynb		1.5_lesson5.ipynb
1.6_generators_and_comprehensions.ipynb		1.6_generators_and_comprehensions.ipynb
1.7.2_decorators.ipynb		1.7.2_decorators.ipynb
1.7.3_class_and_magic_functions.ipynb		1.7.3_class_and_magic_functions.ipynb
1.7_labyrinth_game.ipynb		1.7_labyrinth_game.ipynb
1.8_class_and_inheritence.ipynb		1.8_class_and_inheritence.ipynb
1.9_python_test.ipynb		1.9_python_test.ipynb
2.1.1_linux_lesson1.ipynb		2.1.1_linux_lesson1.ipynb
2.1.2_linux_environment.ipynb		2.1.2_linux_environment.ipynb
2.2_matplot_excercise_LTC_BTC.ipynb		2.2_matplot_excercise_LTC_BTC.ipynb
3.0_mnist_basic_intro.ipynb		3.0_mnist_basic_intro.ipynb
3.1.1_mighty_mlp.ipynb		3.1.1_mighty_mlp.ipynb
3.1.2_how_machine_learns_to_read.ipynb		3.1.2_how_machine_learns_to_read.ipynb
3.1.3_recommender_system.ipynb		3.1.3_recommender_system.ipynb
3.1_nn_2_cnn_2_densenet.ipynb		3.1_nn_2_cnn_2_densenet.ipynb
3.2_understand_rnn_lstm.ipynb		3.2_understand_rnn_lstm.ipynb
3.3_fun_with_pic_and_optimizer.ipynb		3.3_fun_with_pic_and_optimizer.ipynb
3.4_dataset_split.ipynb		3.4_dataset_split.ipynb
3.5_from_keras_to_pytorch.ipynb		3.5_from_keras_to_pytorch.ipynb
3.6_convolution_and_image_problem.ipynb		3.6_convolution_and_image_problem.ipynb
3.7.1_Image_Transformation_Inference.ipynb		3.7.1_Image_Transformation_Inference.ipynb
3.7_Image_transformation.ipynb		3.7_Image_transformation.ipynb
3.9_attentional_rnn_and_hightlights.ipynb		3.9_attentional_rnn_and_hightlights.ipynb
9.0.1_pythorn2mysql_template.ipynb		9.0.1_pythorn2mysql_template.ipynb
README.md		README.md
_config.yml		_config.yml
break-linear-example.ipynb		break-linear-example.ipynb
class_in_python.png		class_in_python.png
correlation_and_convolution.ipynb		correlation_and_convolution.ipynb
distance_query.ipynb		distance_query.ipynb
export_video_to_image.ipynb		export_video_to_image.ipynb
fscore_threshold_explained.ipynb		fscore_threshold_explained.ipynb
how-election2020-hashtagging.ipynb		how-election2020-hashtagging.ipynb
image.png		image.png
pytorch_enhance1_dataset_tutorial.ipynb		pytorch_enhance1_dataset_tutorial.ipynb
pytorch_enhance2_embedding_explained.ipynb		pytorch_enhance2_embedding_explained.ipynb
xarray_01.ipynb		xarray_01.ipynb

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Python for Machine Learning

Python Tutorial

Deep Learning

Originally designed to teach Alex python, thank you for your constant learning enthusiasm

Also created for coderbunker deep learning talk sessions

Experiments

Environment Installation

Libs

Contact Us

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