dl engineer guidebook

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Guide to survival of deep learning engineers

Read online: https://dl.ypw.io
Read online: https://ypwhs.github.io/dl-engineer-guidebook/
Project address: https://github.com/ypwhs/dl-engineer-guidebook

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This book will tell you everything a deep learning engineer needs:

• How to configure a deep learning workstation?
  • CPU
  • Motherboard
  • Graphics Card
  • harddisk
  • Memory
  • power supply
  • Network card
  • Chassis
  • monitor
  • Keyboard and mouse
• Local equipment
  • Windows, Linux or macOS?
  • Mac comparison
  • other
    • Touchpad
    • Mechanical keyboard
    • iPad Pro
    • router
    • NAS
    • Mobile hard drive
    • USB drive
• macOS software
  • terminal
  • Editor
  • Browser
  • Develop software
  • Practical Tools
  • Virtual Machine
• macOS environment
  • Homebrew
  • oh my zsh
  • Essential software
    • terminal
    • Editor
    • Browser
    • Develop software
    • Practical Tools
    • Daily Applications
  • Necessary commands
• Python environment
  • Anaconda
  • Python Library
• Ubuntu installation steps
  • Install Ubuntu
  • Configure ssh
  • Configure sudo password-free and apt source (recommended)
  • Install oh my zsh and commonly used commands (recommended)
  • Install NVIDIA driver, CUDA and cuDNN (divided into two installation methods: apt and run)
  • Install Anaconda and Python libraries
• Ubuntu environment
  • CUDA
  • cuDNN
  • TensorFlow
  • PyTorch
  • Necessary commands
    • curl
    • tmux is used in conjunction with iTerm2
    • screen background run command
• Common Linux commands
  • File viewing
  • Read and write files
  • Packaging compression
  • Permission Management
  • Process Management
  • Disk Management
  • System Management
  • System monitoring
  • Network communication
• CV Learning Resources
  • Open class
  • website
  • books
• Commonly used CV datasets
  • How to use dataset
  • MNIST
  • CIFAR
  • ImageNet
  • VOC
  • COCO
  • CelebA
• Classic models that perform well on ImageNet
  • How to use pre-trained models
    • TensorFlow
    • PyTorch
  • Model paper
• How to use TensorBoard
  • Install
  • use
    • Create a file object (writer)
    • Open TensorBoard Service
    • Visualize the model structure
    • Record scalar
    • Record multiple scalars
    • Record images (images)
    • Complete code
  • Summarize
• Offline Python environment

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