Deep Learning Frameworks in Depth — Deep Learning & Neural Networks R…
Becoming genuinely productive in PyTorch and TensorFlow, not just running tutorials
Steps in Deep Learning Frameworks in Depth
- PyTorch Tensors & Autograd — intermediate · Tensor operations and PyTorch's automatic differentiation engine
- Building Models with nn.Module — intermediate · Defining custom layers and full models as composable PyTorch modules
- Custom Datasets & DataLoaders — intermediate · Wrapping raw data in Dataset/DataLoader classes for efficient, batched training
- Writing Training & Evaluation Loops — intermediate · The standard forward/loss/backward/step loop and tracking metrics correctly
- TensorFlow & Keras Essentials — intermediate · The equivalent high-level workflow in TensorFlow's Keras API
- Mixed Precision & Performance Basics — advanced · Training faster and using less memory with lower-precision floating point
Part of
- Deep Learning & Neural Networks roadmap — the full learning path