This repository contains my practice exercises and implementations using NumPy, which I completed as preparation for learning PyTorch. NumPy is a fundamental library for scientific computing in Python and serves as an excellent foundation for understanding PyTorch's tensor operations and array manipulations.
- To build a strong foundation in numerical computing with NumPy
- To understand array operations, broadcasting, and vectorization
- To prepare for learning PyTorch's tensor operations
- To practice implementing common deep learning operations using NumPy
- Array creation and manipulation
- Mathematical operations
- Broadcasting
- Linear algebra operations
- Random number generation
- Array indexing and slicing
- Performance optimization with vectorized operations
NumPy provides the fundamental building blocks for understanding how tensors work in PyTorch. By mastering NumPy first, I can better understand:
- How tensors are stored and manipulated in memory
- The importance of vectorized operations
- The relationship between CPU and GPU operations
- The mathematical foundations of deep learning
After completing these NumPy exercises, I plan to:
- Transition to PyTorch's tensor operations
- Implement neural networks using PyTorch
- Work on deep learning projects