Neural Network Design

Overview

This skill covers designing and implementing neural network architectures including CNNs, RNNs, Transformers, and ResNets using PyTorch and TensorFlow, with focus on architecture selection, layer composition, and optimization techniques.

When to Use

• Designing custom neural network architectures for computer vision tasks like image classification or object detection
• Building sequence models for time series forecasting, natural language processing, or video analysis
• Implementing transformer-based models for language understanding or generation tasks
• Creating hybrid architectures that combine CNNs, RNNs, and attention mechanisms
• Optimizing network depth, width, and skip connections for better training and performance
• Selecting appropriate activation functions, normalization layers, and regularization techniques

Core Architecture Types

• Feedforward Networks (MLPs): Fully connected layers
• Convolutional Networks (CNNs): Image processing