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Master advanced ML/DS statistics
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Module 11
Advanced Neural Network Architectures
1
Convolutional Layer Fundamentals: Shared Weights and Local Connectivity
Explain how convolutional layers use shared weights and local connectivity to process spatial data.
2
Pooling Layers: Translation Invariance & Dimensionality Reduction
Describe the role of pooling layers in creating translation invariance and reducing dimensionality.
3
Building Your First CNN for Image Classification
Assemble a basic Convolutional Neural Network (CNN) for an image classification task.
4
Residual Connections in Deep Networks
Explain how residual connections (ResNets) enable the training of very deep networks.
5
RNN Architecture for Sequential Data
Describe the architecture of a Recurrent Neural Network (RNN) for processing sequential data.
6
Understanding Vanishing and Exploding Gradients in RNNs
Explain the vanishing and exploding gradient problems that affect simple RNNs.
7
Understanding LSTM Architecture and Vanishing Gradients
Describe the architecture of an LSTM cell and explain how its gates mitigate vanishing gradients.
8
LSTM for Sequential Data Modeling
Apply an LSTM network to model sequential data like time series or text.
Previous module
Neural Network Foundations
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Modern Sequence Modeling with Transformers