Rahma Fourati – författare

Advanced Optimization and Acceleration Techniques for Deep Learning Models provides a comprehensive guide to enhancing deep learning models' efficiency, scalability, and performance, including large language models (LLMs). As AI systems grow in complexity, optimizing their training and deployment has become critical for achieving higher accuracy, faster inference, and reduced computational costs. This book explores cutting-edge optimization strategies, from gradient descent refinements and hyperparameter tuning to model compression, pruning, and hardware acceleration. AI is evolving rapidly, but existing deep learning resources often focus on building models rather than optimizing them for efficiency and scalability. As deep learning applications expand into cloud computing, edge AI, and real-time decision-making, a dedicated resource on optimization is essential. This book addresses this gap by providing a structured approach to making deep learning networks faster, more cost-effective, and more sustainable.Explains the complexity problem in deep learning and explores optimization techniques tailored to different use casesTraining Efficiency: How to accelerate training without compromising accuracy using gradient descent optimizations, adaptive learning rates, and parallel processingModel Deployment & Scalability: How to efficiently deploy deep learning models in cloud environments, edge devices, and mobile platforms using pruning, quantization, and distillationMemory & Computational Constraints: How to reduce model size and inference latency for real-time applications using low-rank factorization, weight sharing, and model compressionSustainability & Green AI: How to design energy-efficient AI systems that balance performance and resource consumption, making AI more accessible and cost-effective