Examples
In this section, you'll find a couple of Jupyter notebooks showcasing the practical usage of Liltorch. These notebooks demonstrate how to effectively leverage the library's functionalities for various deep learning tasks (more to be added).
MNIST Digit Classification with 95% Accuracy
This notebook exemplifies the library's capabilities by achieving 95% accuracy on the classic MNIST handwritten digit classification dataset. The notebook guides you through the process of:
Loading the MNIST Dataset: Learn how to load the MNIST dataset using appropriate libraries within your environment.
Building Your Neural Network: Discover how to construct a neural network architecture suitable for the MNIST task, taking advantage of the library's building blocks.
Training the Network: Explore the training process, including defining the loss function and training parameters.
Evaluating Performance: Witness the evaluation of the trained network's performance on the test set, demonstrating the achieved 95% accuracy.