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First-Time Deep Learning and PyTorch Bootcamp (Easy! From the Basics to the Core Transformers Behind ChatGPT) [Data Analysis/Science Part 3]

This course has been newly designed to help you gradually learn the mathematics and theory needed to understand deep learning, PyTorch-based implementation, transfer learning, and the core GPT Transformer, based on the instructor’s experience of struggling when first learning deep learning.

(4.9) 99 reviews

1,750 learners

Level Basic

Course period Unlimited

  • funcoding
Deep Learning(DL)
Deep Learning(DL)
PyTorch
PyTorch
Machine Learning(ML)
Machine Learning(ML)
Artificial Neural Network
Artificial Neural Network
Vision Transformer
Vision Transformer
Deep Learning(DL)
Deep Learning(DL)
PyTorch
PyTorch
Machine Learning(ML)
Machine Learning(ML)
Artificial Neural Network
Artificial Neural Network
Vision Transformer
Vision Transformer
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funcoding

Sharing changes to the version of PyTorch in the first deep learning and PyTorch bootcamp

Hello. This is Dave Lee from Janjaemi Coding.

The reason is that I am sharing this because there are some parts of the code that may behave slightly differently in recent PyTorch versions.

In recent PyTorch versions, when creating a tensor with FloatTensor() , each tensor is initialized to 0, not a random value.

(Because the internal memory is allocated but the values are not initialized to “0” or random values, random-looking values (actually garbage values that were left in the memory) were entered, but the latest version forces them to be initialized to 0.)

Therefore, when creating a simple tensor without arbitrarily specifying a specific value, 0 is specified as the initial value when outputting each tensor, not a random value, so it comes out as 0 when output. This is normal operation, so it would be good to refer to only this part.

We have updated the entire data by including an explanation of this part in the code data today.

thank you

Dave Lee Dream of residual fun coding

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