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Machine Learning Syllabus Overview
·
Module 9
Image Classification with Neural Networks
1
Building an Image Classifier with PyTorch
Construct a multi-layer feedforward neural network in PyTorch for an image classification task.
2
Non-Linear Activation Functions for Complex Patterns
Explain the role of non-linear activation functions (e.g., ReLU) in enabling a network to learn complex patterns.
3
Softmax: From Logits to Probabilities
Describe how the softmax function converts final layer outputs (logits) into class probabilities.
4
Backpropagation: Intuitive Error Gradient Flow
Explain backpropagation intuitively as the process of propagating error gradients back through the network to update weights.
5
PyTorch Multiclass Image Classification
Train a multiclass classification model using the PyTorch training loop on a standard image dataset (e.g., MNIST).
Multiclass Neural Network Evaluation
Evaluate a multiclass neural network model using a confusion matrix and micro/macro-averaged metrics.
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