A deep engineering dive into forward mode and reverse mode automatic differentiation, computational graphs, PyTorch autograd internals, custom gradient functions, and when to use torch.no_grad().
NumPy, Pandas, SciPy, Matplotlib, scikit-learn, PyTorch, and JAX - the complete Python stack for AI/ML engineering.
Master the complete ML Python stack - NumPy, Pandas, scikit-learn, PyTorch, HuggingFace, and Weights & Biases - the tools every ML engineer uses every day.
PyTorch tensors, autograd, neural network modules, training loops, GPU acceleration, and production patterns for deep learning.
