Chapter 32 sections from Deep Learning with PyTorch.
8 items
Modern deep learning systems often improve when we increase three quantities: model size, dataset size, and compute. This empirical regularity is called a scaling law.
Modern deep learning systems are constrained by compute, memory, bandwidth, latency, and energy. As models become larger, efficiency becomes a central engineering problem rather than a secondary optimization.
Scientific deep learning applies neural networks and differentiable computation to scientific and engineering problems.
Robotics and embodied AI study learning systems that act in the physical world.
Deep learning has made large empirical gains, but many scientific and engineering questions remain open.
Deep learning systems have progressed from small task-specific models to large multimodal foundation systems capable of perception, language understanding, reasoning, planning, generation, and interaction.
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This chapter covered scaling, efficient systems, scientific AI, robotics, and open research problems. The following books, papers, and resources provide deeper treatment of these areas.