2. Boosting Deep Learning Performance: Parallel and Distributed Training Strategies in PyTorch
   PyTorch offers functionalities for parallelizing model training across multiple GPUs on a single machine. This approach is ideal when you have a large dataset or a complex model
3. Optimizing Data Sharing in Python Multiprocessing: Shared Memory vs. Alternatives
   In Python's multiprocessing module, you create separate processes to handle tasks concurrently, leveraging multiple CPU cores