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Introduction to AI

Machine Learning

Deep Learning

NLP & LLMs

Computer Vision

Generative AI

Reinforcement Learning

AI Ethics & Governance

Tools & Frameworks

General

AI Performance Optimization

Optimizing AI models is crucial for deploying them in production environments where speed and resource consumption are critical.

Optimization Strategies

  • Quantization: Reducing the precision of weights (e.g., from 32-bit to 8-bit).
  • Pruning: Removing less important neurons or connections from a network.
  • Knowledge Distillation: Training a smaller “student” model to mimic a larger “teacher” model.
  • Architecture Search: Automatically finding the most efficient network design.

Hardware Acceleration

  • GPUs (Graphics Processing Units): Standard for training and high-throughput inference.
  • TPUs (Tensor Processing Units): Google’s custom chips for machine learning.
  • NPU (Neural Processing Unit): Specialized hardware in mobile devices for AI tasks.

Measurement Metrics

  • Inference Latency: Time taken for a single prediction.
  • Throughput: Number of predictions per second.
  • Model Size: Storage space required for the model parameters.