instruction: Create a Dockerfile for fundamentals
output: ```dockerfile
  from scratch or using
  pre-trained models, facilitating a wide range of ML applications directly in web
  environments. TensorFlow.js offers efficient computation, making sophisticated
  ML tasks accessible to web developers without deep ML expertise.Why Use TensorFlow.js and Docker together?Environment consistency and simplified deployment: Docker packages
  TensorFlow.js applications and their dependencies into containers, ensuring
  consistent runs across all environments and simplifying deployment.Efficient development and easy scaling: Docker enhances development efficiency
  with features like hot reloading and facilitates easy scaling of -
  TensorFlow.js applications using orchestration tools like Kubernetes.Isolation and enhanced security: Docker isolates TensorFlow.js applications in
  secure environments, minimizing conflicts and security vulnerabilities while
  running applications with limited permissions.Get and run the sample applicationIn a terminal, clone the sample application using the following command.
  
  $ git clone https://github.com/harsh4870/TensorJS-Face-Detection
  After cloning the application, you'll notice the application has a Dockerfile.
  This Dockerfile lets you build and run the application locally with nothing more
  than Docker.Before you run the application as a container, you must build it into an image.
  Run the following command inside the TensorJS-Face-Detection directory to
  build an image named face-detection-tensorjs.
  
  $ docker build -t face-detection-tensorjs .
  The command builds the application into an image. Depending on your network
  connection, it can take several minutes to download the necessary components the
  first time you run the command.To run the image as a container, run the following command in a terminal.
  
  $ docker run -p 80:80 face-detection-tensorjs
  The command runs the container and maps port 80 in the container to port 80 on
  your system.Once the application is running, open a web browser and access the application
  at http://localhost:80. You may need to grant access to
  your webcam for the application.In the web application, you can change the backend to use one of the following:WASMWebGLCPUTo stop the application, press ctrl+c in the terminal.About the applicationThe sample application performs real-time face detection using
  MediaPipe, a comprehensive framework
  for building multimodal machine learning pipelines. It's specifically using the
  BlazeFace model, a lightweight model for detecting faces in images.In the context of TensorFlow.js or similar web-based machine learning
  frameworks, the WASM, WebGL, and CPU backends can be used to
  execute operations. Each of these backends utilizes different resources and
  technologies available in modern browsers and has its strengths and limitations.
  The following sections are a brief breakdown of the different backends.WASMWebAssembly (WASM) is a low-level, assembly-like language with a compact binary
  format that runs at near-native speed in web browsers. It allows code written in
  languages like C/C++ to be compiled into a binary that can be executed in the
  browser.It's a good choice when high performance is required, and either the WebGL
  backend is not supported or you want consistent performance across all devices
  without relying on the GPU.WebGLWebGL is a browser API that allows for GPU-accelerated usage of physics and
  image processing and effects as part of the web page canvas.WebGL is well-suited for operations that are parallelizable and can
  significantly benefit
  ```