DeepLearning-Tutorial/legacy_materials at main · Sangh0/DeepLearning-Tutorial

Name		Name	Last commit message	Last commit date
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1_1_linear_regression_and_classification.ipynb		1_1_linear_regression_and_classification.ipynb
1_2_gradient_descent_algorithm.ipynb		1_2_gradient_descent_algorithm.ipynb
2_dnn_mnist_pytorch.py		2_dnn_mnist_pytorch.py
2_dnn_with_mnist.ipynb		2_dnn_with_mnist.ipynb
3_cnn_mnist_pytorch.py		3_cnn_mnist_pytorch.py
3_cnn_with_mnist.ipynb		3_cnn_with_mnist.ipynb
4_cnn_with_overfitting.ipynb		4_cnn_with_overfitting.ipynb
5_vgg16.ipynb		5_vgg16.ipynb
5_vgg_pytorch.py		5_vgg_pytorch.py
README.md		README.md

README.md

Contents

Chapter 1. Implement simple machine learning algorithm using scikit-learn and numpy framework
- chap 1-1. Linear Regression and Classification with Scikit-Learn, click
- chap 1-2. Implement Gradient Descent using only numpy framework click
Chatper 2. Training and performance evaluation of an MLP model using MNIST benchmark
- implemented with PyTorch
- implemented with Tensorflow and Keras
Chapter 3. Training and performance evaluation of an CNN model using MNIST benchmark
- implemented with PyTorch
- implemented with Tensorflow and Keras
Chapter 4. Methods to address Over-fitting during AI model training
- material implemented with Tensorflow
Chapter 5. Implement VGG16 network
- implemented with PyTorch
- implemented with Tensorflwo and Keras