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Full Stack AI Developer Roadmap
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Module 7
Deep Learning Foundations
1
Neural Network Fundamentals
Explain artificial neurons, activation functions, and network architecture
2
Building a Feedforward Network in PyTorch
Define a feedforward neural network using PyTorch
3
Backpropagation and Gradient Descent
Explain backpropagation and compute gradients for a simple network
4
SGD and Adam Optimizers for Neural Networks
Train neural networks using SGD and Adam optimizers
5
Learning Curves for Overfitting and Underfitting
Diagnose overfitting and underfitting using learning curves
Dropout and Batch Normalization
Apply regularization techniques: dropout and batch normalization
7
PyTorch Model Persistence
Save, load, and checkpoint PyTorch models
8
Accelerating Deep Learning with GPUs
Use GPU acceleration for training deep learning models
Previous module
Machine Learning Fundamentals
Next module
Computer Vision with CNNs