8. Topics for Exam 1
Topics for Exam 1
Foundations:
  • Python, NumPy, and Pytorch
  • Vector, matrix, and tensor operations
  • Equations of lines and planes in multi-dimensional space (hyperplanes)
Neural Network Basics:
  • Neuron model and network architectures
  • Single neuron and activation functions
  • Layers of neurons, weight matrices, and biases
  • Multi-layer neural network architecture
  • Decision boundaries and their relationship to hyperplanes
Computational Graphs:
  • Construction and interpretation of computational graphs
  • Forward pass and backpropagation (backward pass)
Loss functions: 
  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Hinge loss (SVM)
  • Cross-entropy loss
PyTorch Workflow:
  • Data transforms
  • Datasets
  • Dataloaders
  • Model definition
  • Loss functions
  • Optimizers and learning rate schedulers
  • Training loop
  • Evaluation loop
  • Performance metrics and visualization (plots)
  • Saving/loading a checkpoint
Convolutional Neural Networks (CNN):
  • Creating and analyzing CNNs using PyTorch
  • Convolutional filters, padding, and stride.
  • Layer types: convolutional, pooling, flattening, and fully connected
  • Determining shape of the weight matrix for each layer
  • Determining shape of the output for each layer
  • Determining number of parameters
Coding Component
The exam will include coding questions requiring you to write or complete Python code (using NumPy or PyTorch) based on concepts covered in lectures or assignments.
These questions may be similar to or derived from portions of your assignments.
 
 
 
 
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