Dive into a comprehensive 52-minute workshop on Tensors in deep learning using PyTorch. Explore over 40 Tensor operations on CPU and GPU, covering topics from basic tensor declarations to advanced operations. Learn about tensor data types, conversions, element-wise operations, logical operations, indexing, reshaping, and device management. Gain hands-on experience with a Jupyter notebook, understanding the building blocks of deep learning and practical applications of tensors in Python. Perfect for beginners looking to master tensor fundamentals and their implementation in PyTorch.
Everything You Need to Know About Tensors in Deep Learning With PyTorch
Overview
Syllabus
- Workshop Introduction
- Tensor Introduction
- Building blocks of Deep Learning
- Input data as Tensor
- Tensors as higher degree matrix
- Declaration of Tensors in PyTorch
- Tensor Data Types
- Tensors as Python List and Pandas DF
- Tensors from NumPy ndarray
- torch.ones_like function
- torch.zeros_like function
- Tensor to NumPy ndarray conversion
- Tensors Operations
- Matrix multiplication on Tensors
- Transpose
- Element-wise Operations on Tensors
- Element-wise Multiplication
- torch.matmulT1, T2, out
- Element-wise Division
- Element-wise Addition
- Element-wise Subtraction
- Element-wise Square-root
- Tensor Aggregation
- Tensor In-place operation
- Tensor Logical Operation
- Bitwise or Shift Operations
- Indexing and Slicing in Tensor
- Reshaping Tensors
- Tensor Concatenation
- Tensor Devices CPU or GPU
- GPU in Google Colab
- Memory limitation with Tensors
- Tensor on GPU
- Tensor from CPU to GPU and vice-versa
- Tensor bridge with NumPy
- Recap
Taught by
Prodramp
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