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5. **Crossover:** Combine the world of *Prince of Persia* with another franchise. How would Shahdee fare in a different setting, and how would her presence impact the story?
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This is arguably one of the most exciting aspects of **how to use Google Colaboratory** – the free access to powerful hardware accelerators like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). For computationally intensive tasks, especially in deep learning and machine learning, training models on a regular CPU can take hours, days, or even weeks. GPUs and TPUs are designed to perform massive parallel computations, drastically speeding up these processes. To enable them, navigate to `Runtime > Change runtime type`. In the `Hardware accelerator` dropdown menu, select `GPU` or `TPU`. Click `Save`, and Colab will reconnect your runtime with the chosen accelerator. You'll know it's working when you see CUDA (for GPU) or TPU-specific libraries being utilized in your code. For example, when using TensorFlow or PyTorch, you'll often see messages indicating that the backend is using the GPU. Remember that the availability of these accelerators can sometimes be limited, and there are usage limits to prevent abuse. Also, not all code will automatically benefit from a GPU/TPU; it primarily helps with matrix operations common in neural networks. So, if you're doing heavy numerical computation, switching to a GPU or TPU is a must-know technique for **how to use Google Colaboratory** efficiently.
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4. **Preparing Your Bid:**
