Multi-Layer Perceptrons and MNIST Digit Classification Using PyTorch - Lab 2.2

Learn to implement Multi-Layer Perceptrons (MLPs) from scratch and apply them to digit classification in this hands-on lab video tutorial. Begin with fundamental matrix operations using NumPy to build MLP layers, then progress to utilizing PyTorch's capabilities for the MNIST dataset. Master essential concepts including tensor reshaping, bias implementation, activation functions, and PyTorch linear layers while working through practical examples. Explore GPU acceleration for neural networks and gain hands-on experience with a complete Colab notebook. Follow along with clearly marked sections covering matrix multiplication basics, input tensor manipulation, activation functions, and the complete workflow for classifying handwritten digits. Access additional resources on scientific computing in Python, including an in-depth guide to NumPy and Matplotlib, to further enhance your understanding of neural network implementations.