Modifying a computer for deep learning involves upgrading its hardware and optimizing its software. Ensure the system has a high-performance GPU (e.g., NVIDIA RTX 3090 or A100) with sufficient VRAM (8–24 GB) for handling large models and datasets.
Equip the system with at least 16 GB of RAM and a fast CPU (e.g., Intel i7/i9 or AMD Ryzen) to manage preprocessing and parallel tasks. Use an SSD for faster data access and storage. Install optimized frameworks like TensorFlow or PyTorch and ensure GPU drivers (e.g., CUDA, cuDNN) are properly configured.
Software tools like Docker or Anaconda simplify environment setup, while cloud platforms like AWS or Google Cloud provide scalable resources for intensive workloads.