Learn Docker Quickly - Machine Learning Project for Beginners

Dive into Docker fundamentals and machine learning in this comprehensive 26-minute tutorial. Explore containers, images, and Dockerfiles through clear visualizations and hands-on examples. Understand the logic behind Docker components, their problem-solving capabilities, and the consequences of not using them. Develop a simple machine learning program using Huggingface Transformers library, build a custom Docker image based on Jupyter Tensorflow Notebook, and learn to deploy projects to DockerHub. By the end, gain practical experience in creating a video captions translating software and acquire a thorough understanding of Docker, regardless of prior programming experience. Topics covered include Docker installation, image management, container operations, MNIST dataset handling with Tensorflow, Docker Compose usage, Dockerfile creation, text translation with Transformers, and pushing images to remote repositories.

00:00 - | Intro
00:40 - | What is Docker? What are containers?
02:21 - | Install Docker
03:06 - | What are Docker Images?
04:14 - | Search and Pull Images
05:20 - | Run Container
06:08 - | Expose Container Port
07:18 - | Load MNIST Dataset with Tensorflow
08:10 - | Plot MNIST sample
09:47 - | Run Containers with Docker Compose
11:11 - | Replace Jupyter Token with Password
12:02 - | Mount Drive
13:07 - | Build Images with Docker Compose
13:32 - | Dockerfile
15:29 - | Translate Text with Transformers
17:06 - | copy files from system to image
20:52 - | create public repository on DockerHub
21:14 - | push local image to remote repository
23:04 - | clean up containers and images
25:11 - | thank you for watching!