PyTorch for AI Research Scientists: Building & Training Custom Models from Scratch
Master PyTorch's ecosystem to design, implement, and scale custom deep learning models for cutting-edge AI research and development.
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Foundational PyTorch for Custom Model Development
Unit 1: PyTorch Building Blocks
Tensors: The Core Data
Autograd: The Magic
Unit 2: Crafting Custom Models
nn.Module: Your Model
Modern Layers & Beyond
Unit 3: Data Handling & Training Loop
Dataset: Your Data Source
DataLoader: Batching Up
Optimizers & Loss Fns
The Training Loop
Advanced PyTorch for Scalable AI Research
Unit 1: Model Evaluation and Optimization
Metrics that Matter
Debugging Deep Learning
Hyperparameter Tuning
Saving & Loading Models
Unit 2: Scaling Training with Distributed PyTorch
Intro to Distributed PyTorch
DataParallel in Action
DistributedDataParallel
Optimizing Distributed Training