🧠Deep Learning
🔧
Deep Learning Frameworks
Master PyTorch and TensorFlow for building and training deep learning models
Prerequisites
🌱
Beginner
BeginnerGetting started with frameworks
What to Learn
- •Tensors and automatic differentiation
- •Building models with nn.Module/keras.Model
- •Data loading and batching
- •Training loops and optimization
- •Saving and loading models
Resources
- 📚PyTorch official tutorials
- 📚TensorFlow/Keras getting started
- 📚FastAI practical deep learning
🌿
Intermediate
IntermediateProduction-ready framework usage
What to Learn
- •Custom layers and loss functions
- •Distributed training (DDP, strategy)
- •Mixed precision training
- •Debugging and profiling
- •TorchScript and SavedModel export
Resources
- 📚PyTorch distributed training guide
- 📚TensorFlow Extended (TFX)
- 📚Lightning and Keras best practices
🌳
Advanced
AdvancedAdvanced framework internals
What to Learn
- •Custom autograd functions
- •JIT compilation and optimization
- •Framework interoperability (ONNX)
- •Contributing to frameworks
- •Custom CUDA kernels
Resources
- 📚PyTorch internals blog posts
- 📚CUDA programming for deep learning
- 📚Framework source code exploration