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  01 - Introduction

   01 - Deep learning with PyTorch.srt

   02 - What you should know.srt

   03 - Tour of CoderPad.srt

  02 - 1. PyTorch Overview and Introduction to Google Colaboratory

   01 - Introduction to deep learning.srt

   02 - Why should you use PyTorch.srt

   03 - Google Colaboratory basics.srt

  03 - 2. Tensors

   01 - Introduction to tensors.srt

   02 - Creating a tensor CPU example.srt

   03 - Creating a tensor GPU example.srt

   04 - Moving tensors between CPUs and GPUs.srt

  04 - 3. Creating Tensors

   01 - Different ways to create tensors.srt

   02 - Tensor attributes.srt

   03 - Tensor data types.srt

   04 - Creating tensors from random samples.srt

   05 - Creating tensors like other tensors.srt

   06 - Solution Create tensors.srt

  05 - 4. Manipulate Tensors

   01 - Tensor operations.srt

   02 - Mathematical functions.srt

   03 - Linear algebra operations.srt

   04 - Automatic differentiation (Autograd).srt

   05 - Solution Split tensors to form new tensors.srt

  06 - 5. Developing a Deep Learning Model

   01 - Introduction to the DL training process.srt

   02 - Data preparation.srt

   03 - Data loading.srt

   04 - Data transforms.srt

   05 - Data batching.srt

   06 - Model development and training.srt

   07 - Validation and testing.srt

  07 - Conclusion

   01 - Next steps.srt

  Bonus Resources.txt