Everything You Need to Know About Tensors in Deep Learning With PyTorch

Everything You Need to Know About Tensors in Deep Learning With PyTorch

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- Workshop Introduction

1 of 36

1 of 36

- Workshop Introduction

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Everything You Need to Know About Tensors in Deep Learning With PyTorch

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  1. 1 - Workshop Introduction
  2. 2 - Tensor Introduction
  3. 3 - Building blocks of Deep Learning
  4. 4 - Input data as Tensor
  5. 5 - Tensors as higher degree matrix
  6. 6 - Declaration of Tensors in PyTorch
  7. 7 - Tensor Data Types
  8. 8 - Tensors as Python List and Pandas DF
  9. 9 - Tensors from NumPy ndarray
  10. 10 - torch.ones_like function
  11. 11 - torch.zeros_like function
  12. 12 - Tensor to NumPy ndarray conversion
  13. 13 - Tensors Operations
  14. 14 - Matrix multiplication on Tensors
  15. 15 - Transpose
  16. 16 - Element-wise Operations on Tensors
  17. 17 - Element-wise Multiplication
  18. 18 - torch.matmulT1, T2, out
  19. 19 - Element-wise Division
  20. 20 - Element-wise Addition
  21. 21 - Element-wise Subtraction
  22. 22 - Element-wise Square-root
  23. 23 - Tensor Aggregation
  24. 24 - Tensor In-place operation
  25. 25 - Tensor Logical Operation
  26. 26 - Bitwise or Shift Operations
  27. 27 - Indexing and Slicing in Tensor
  28. 28 - Reshaping Tensors
  29. 29 - Tensor Concatenation
  30. 30 - Tensor Devices CPU or GPU
  31. 31 - GPU in Google Colab
  32. 32 - Memory limitation with Tensors
  33. 33 - Tensor on GPU
  34. 34 - Tensor from CPU to GPU and vice-versa
  35. 35 - Tensor bridge with NumPy
  36. 36 - Recap

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