Back to AI DashboardModule 2: Neural Network Foundations
AI Engineer Track
Module 2: Neural Network Foundations
Deconstruct deep learning layers, weights, biases, backpropagation, and activation/loss functions from scratch.
Syllabus Modules
Neuron LayersComing Soon
Understand single-node perceptrons and multi-layer fully connected structures.
Total Lessons: 0Explore Module
Weights and BiasesComing Soon
Study weight initialization strategies and bias thresholds scaling.
Total Lessons: 0Explore Module
Activation FunctionsComing Soon
Deconstruct Sigmoid, Tanh, ReLU, and GeLU gradients properties.
Total Lessons: 0Explore Module
Loss FunctionsComing Soon
Map Mean Squared Error and Cross-Entropy loss landscapes math.
Total Lessons: 0Explore Module
OptimizersComing Soon
Examine SGD, Momentum, RMSProp, and Adam convergence rates.
Total Lessons: 0Explore Module
Backpropagation MechanicsComing Soon
Derive chain rule partial derivatives backpropagating weights gradients.
Total Lessons: 0Explore Module
Embedding LayersComing Soon
Learn lookups mapping integers to continuous dense vector states.
Total Lessons: 0Explore Module
Track Progress
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Learning Outcomes
- Implement forward pass matrices from scratch
- Compute partial derivatives for backpropagation
- Tune weight matrices using SGD and Adam optimizers
Interview Defense
- Derive backpropagation rules mathematically
- Compare ReLU, GeLU, and Sigmoid activation functions under gradients vanishing