Although you don't need to know matrix algebra to understand the ideas behind neural networks, if you want to code them or read the latest manuscripts about the field, then you'll need to understand matrix algebra. This video teaches the essential topics in matrix algebra and shows how a neural network can be written as a matrix equation, and then shows how understand PyTorch documentation, error messages and the equations for Attention, which is the fundamental concept behind ChatGPT.
Note: If you want to learn more about neural networks...
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...backpropagation...
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...the ReLU activation function...
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...tensors...
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...SoftMax...
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...Transformers and Attention...
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0:00 Awesome song and introduction
2:35 Introduction to linear transformations
5:57 Linear transformations in matrix notation
7:34 Matrix multiplication
11:03 Matrix multiplication consolidates a sequence of linear transformations
13: 46 Order matters for matrix multiplication
15:18 Transposing a matrix
16:37 Matrix notation and equations
18:51 Using matrix equations to describe a neural network
24:26 nn.Linear() documentation explained
26:38 1-D vs 2-D error messages explained
27:17 The matrix equation for Attention explained
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