machine_learning

Machine learning algorithms and experiments with Python 🤖

Inspriation for the code in this repository comes from many places, including but not limited to:

Machine Learning in Python - Bowles
Machine Learning with Scikit-learn and Tensorflow - Geron
Deep Learning with Python - Chollet
Python Data Science Handbook - VanderPlas
Machine Learning with Python - Muller & Guido

I highly recommend all of these books.

Practice notebooks

I have included template notebooks for various machine learning projects with code blocks omitted, along with the fully functional versions for comparison. I find these very useful for practicing frameworks, and committing various techniques to memory.

Jupyter notebook with Tensorflow via Docker container

Requirements:

1) Nvidia GPU and up-to-date driver installed

Install Docker:

wget -qO- https://get.docker.com/ | sh

Add non-root user to Unix group:

sudo usermod -aG docker your-name

Install Nvidia-Docker support:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -

curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

sudo systemctl restart docker

Pull desired Docker image:

docker pull tensorflow/tensorflow:latest-gpu-py3-jupyter

Build & Run Docker container (first time):

docker run --gpus all -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/tensorflow:/notebooks --name tf tensorflow/tensorflow:latest-gpu-py3-jupyter

This command sets up network instructions, creates a local directory to keep saved notebook data, builds the container and saves it as "tf" on the host machine.

Escape to Host terminal

ctrl pq

-it flags specify an interactive terminal session

Stop Docker container:

docker container stop tf

Start Docker container:

docker start -i tf

naivoder/machine_learning

machine_learning