pytorch in 1 hour

.shape → a tuple describing the dimensions #1 debugging tool .device → where the tensor lives. cpu or cuda (GPU) .dtype → the data type of the numbers. The default is float32 (because of backprop and gradients)

model weights and biases → float32 (standard)

Autograd - Automatic Differentiation

• it is python’s built in gradient calculator, and can be turned on using requires_grad=True
• to tell pytorch a tensor is a learnable param we must set requires_grad=True, doing so pytorch tracks every single operation on that tensor!

The difference between * and @ in pytorch

• for @ multiplication m1 col = m2 rows

• when building a linear layer always use @ → y = X@W + b

dim arg

selecting data - basic and custom

making forward pass

• model’s first guess
• simple linear regression ŷ = XW + b The magic command - loss.backward()
  • calculates the gradient of the loss wrt to our weight “W”
  • the gradient of the loss wrt to our Bias ‘b’

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