This section focuses on implementing a simple regression model using quadratic equations and optimizing it with PyTorch.
- Function Creation: Define the target function as
target = Ax² + Bx + Cand the prediction function aspredict = A'x² + B'x + C'. - Loss Function: Implement the Mean Squared Error (MSE) Loss as
MSELoss = 0.5 * (target - predict)². - Regression with PyTorch: Utilize the PyTorch API to minimize the loss and make the prediction as close as possible to the target.
In this section, we implement and train the LeNet-5 neural network using the MNIST dataset.
- Neural Network Creation: Construct the LeNet-5 architecture using PyTorch API.
- Training: Train the network using the MNIST dataset.
- Testing: Evaluate the training results and model performance.
Working with a custom dataset, training models like VGG-16 or ResNet18.
- Dataset Access: Load and preprocess the custom dataset using PyTorch API.
- Model Training: Train models like VGG-16 or ResNet18 on the custom dataset.
Each section provides practical insights into the basics of machine learning and deep learning, leveraging PyTorch framework.