Neural Networks from Scratch (Jupyter Notebook)

This Jupyter Notebook adaptation of Neural Networks from Scratch in Python by Harrison and Daniel was developed by Minh-Chien Trinh for use as teaching material in the graduate courses Deep Learning (Spring 2024) and Deep Learning for Computer Vision (Spring 2025) at Jeonbuk National University.

Name		Name	Last commit message	Last commit date
Latest commit History 34 Commits
image		image
.DS_Store		.DS_Store
.gitignore		.gitignore
0_set_up_working_environments.ipynb		0_set_up_working_environments.ipynb
10_optimizers.ipynb		10_optimizers.ipynb
11_testing_with_out_of_sample_data.ipynb		11_testing_with_out_of_sample_data.ipynb
12_validation_data.ipynb		12_validation_data.ipynb
13_training_dataset.ipynb		13_training_dataset.ipynb
14_L1_and_L2_Regularization.ipynb		14_L1_and_L2_Regularization.ipynb
15_dropout.ipynb		15_dropout.ipynb
16_binary_logistic_regression.ipynb		16_binary_logistic_regression.ipynb
17_regression.ipynb		17_regression.ipynb
18_model_object.ipynb		18_model_object.ipynb
1_introducing_neural_networks.ipynb		1_introducing_neural_networks.ipynb
2_coding_our_first_neurons.ipynb		2_coding_our_first_neurons.ipynb
3_adding_layers.ipynb		3_adding_layers.ipynb
4_activation_functions.ipynb		4_activation_functions.ipynb
5_calculating_network_error_with_loss.ipynb		5_calculating_network_error_with_loss.ipynb
6_introducing_optimization.ipynb		6_introducing_optimization.ipynb
7_derivatives.ipynb		7_derivatives.ipynb
8_gradients_pde_chainrule.ipynb		8_gradients_pde_chainrule.ipynb
9_backpropagation.ipynb		9_backpropagation.ipynb
README.md		README.md

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