Deep Learning Theroy

Summary of deep learning theory
总共47个章节，为了阅读美观，去掉的对应的文件夹，直接整理成文档内容（按序）

01-feedforward_network/
02-back_propagation/
03-bp_example_demo/
04-convolution_neural_network/
05-deep_learning_model/
06-pytorch_install/
07-operators/
08-activation_functions/
09-recurrent_neural_network/
10-seq2seq/
11-attentions/
12-weight-initialization/
13-optimizers/
14-regularization/
15-deep-learning-tuning-guide/
20-pytorch-tensor/
21-pytorch-autograd/
22-pytorch-module/
23-training-example-1/
24-pytorch-optimizer/
25-pytorch-lr-scheduler/
26-pytorch-dataloader/
27-pytorch-model-save/
28-pytorch-tensorboard/
29-pytorch-graph-mode/
30-training-example-2/
40-nlp-bert/
41-nlp-t5/
42-stable-diffusion/
44-scaling-law/
45-distribute-training/
46-nlp-llama/
47-nlp-deepseek/

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后续有过修改