🖥️ 1. CPU vs GPU: How They Work for Deep Learning

✅ CPU (Central Processing Unit)

• Best for: General-purpose tasks, branching logic, and sequential operations.
• Strengths:
  • Fewer cores (4–64 typical), but each is powerful and optimized for diverse tasks.
  • Large instruction sets and sophisticated caching for flexible computing.
• Limitations:
  • Small cache sizes compared to GPUs.
  • Not as efficient for massive matrix operations or parallel tasks.

✅ GPU (Graphics Processing Unit)

• Best for: Highly parallel tasks like matrix multiplications, convolutions, and large batch operations.
• Strengths:
  • Thousands of smaller cores optimized for parallel processing.
  • Larger combined cache and memory bandwidth to handle lots of data streams at once.
• Limitations:
  • Less flexible for non-parallel workloads.
  • Cores can’t work independently on diverse tasks — they shine when many cores work on the same type of computation.

💡 Quick Insight

In deep learning, training neural networks often means multiplying huge matrices and applying vectorized operations across many examples — exactly what GPUs excel at!

🛠️ 2. Deep Learning Software Ecosystem

✅ Common Deep Learning Frameworks:

• TensorFlow & Keras: Flexible, scalable, and great for production pipelines.
• PyTorch: Widely loved by researchers for its dynamic computation graph and Pythonic feel.
• MXNet, JAX, PaddlePaddle: Other modern frameworks that provide efficient GPU support.
• ONNX: Open Neural Network Exchange — helps models work across frameworks.

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