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