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PyTorch Essential Training: Deep Learning

سرفصل های دوره

PyTorch is the most flexible and expressive library for deep learning, and offers simple Python API, GPU support, and flexibility. It’s designed to load data, apply transforms, and build deep learning models with just a few lines of code. Many machine learning developers and researchers use PyTorch to accelerate deep learning research, experimentation, and prototyping. In this course, software developer Terezija Semenski teaches you the important features of PyTorch with a hands-on approach to help you develop the skills you need to dive into your deep learning projects.

This course includes Code Challenges powered by CoderPad. Code Challenges are interactive coding exercises with real-time feedback, so you can get hands-on coding practice alongside the course content to advance your programming skills.


01 - Introduction
  • 01 - Deep learning with PyTorch
  • 02 - What you should know
  • 03 - Tour of CoderPad

  • 02 - 1. PyTorch Overview and Introduction to Google Colaboratory
  • 01 - Introduction to deep learning
  • 02 - Why should you use PyTorch
  • 03 - Google Colaboratory basics

  • 03 - 2. Tensors
  • 01 - Introduction to tensors
  • 02 - Creating a tensor CPU example
  • 03 - Creating a tensor GPU example
  • 04 - Moving tensors between CPUs and GPUs

  • 04 - 3. Creating Tensors
  • 01 - Different ways to create tensors
  • 02 - Tensor attributes
  • 03 - Tensor data types
  • 04 - Creating tensors from random samples
  • 05 - Creating tensors like other tensors
  • 06 - Solution Create tensors

  • 05 - 4. Manipulate Tensors
  • 01 - Tensor operations
  • 02 - Mathematical functions
  • 03 - Linear algebra operations
  • 04 - Automatic differentiation (Autograd)
  • 05 - Solution Split tensors to form new tensors

  • 06 - 5. Developing a Deep Learning Model
  • 01 - Introduction to the DL training process
  • 02 - Data preparation
  • 03 - Data loading
  • 04 - Data transforms
  • 05 - Data batching
  • 06 - Model development and training
  • 07 - Validation and testing

  • 07 - Conclusion
  • 01 - Next steps
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