PyTorch Deep Learning
Tensors, autograd, neural networks and training loops with PyTorch
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Course Content
Foundations
Core concepts and groundwork
Introduction to PyTorch and the Deep Learning Ecosystem
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Tensors: Creation, Operations, and GPU Acceleration
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Autograd and Automatic Differentiation
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Building Neural Networks with nn.Module
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Loss Functions and Optimizers
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Training and Validation Loops
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Core Skills
Essential techniques and patterns
Data Loading with Dataset and DataLoader
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Transfer Learning with Pretrained Models
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Convolutional Neural Networks (CNNs)
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Foundational Skills Exercise
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Recurrent Neural Networks and LSTMs
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Attention Mechanisms and Transformers in PyTorch
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Applied Practice
Hands-on, real-world scenarios
Custom Loss Functions and Metrics
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Regularization: Dropout, Batch Norm, Weight Decay
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Learning Rate Scheduling and Warm-Up Strategies
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Mixed Precision Training with torch.cuda.amp
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Distributed Training with DistributedDataParallel
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Intermediate Skills Checkpoint
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Advanced Topics
Deeper, more complex material
PyTorch Lightning for Clean Training Code
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End-to-End Project: Image Classification Pipeline
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Object Detection with PyTorch
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Semantic Segmentation Models
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Time Series Forecasting with PyTorch
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Generative Adversarial Networks (GANs)
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Production & Scale
Building for the real world
Advanced Techniques Exercise
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Variational Autoencoders (VAEs)
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Model Pruning and Quantization
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TorchScript and Model Export
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ONNX Export and Cross-Platform Deployment
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Optimization Exercise: Latency and Throughput
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Mastery & Capstone
Projects and final review
100% Free · All lessons unlocked
Topic Overview
What you'll learn
- Core concepts and fundamentals of PyTorch Deep Learning
- Industry best practices and design patterns
- Hands-on exercises with real-world scenarios
- Performance optimization and advanced techniques
- Building production-ready applications