Future of Neural Networks in Data Science
Integration with Quantum Computing: May accelerate computations and solve larger problems.
Neuro-Symbolic AI: Combining neural networks with rule-based systems for better interpretability.
Energy-Efficient Models: Techniques like model pruning and quantization to reduce resource usage.
AI for Edge Devices: Running neural networks on devices with limited computational power.

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Neural Network Frameworks for Data Science
TensorFlow: Google’s framework for building scalable models.
PyTorch: Known for its dynamic computation graph and flexibility.
Keras: High-level API for quick prototyping.
MXNet and Caffe: Efficient frameworks for deep learning.

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