What factors have led to significant breakthroughs in Deep Learning?

Question

Options

• AAdvances in hardware, availability of fast internet connections, and improvements in training
• BAdvances in sensors, availability of large datasets, and improvements to the "Bag of Words"
• CAdvances in hardware, availability of large datasets, and improvements in training algorithms.
• DAdvances in smartphones, social media sites, and improvements in statistical techniques.

• A
  6% (2)
• B
  3% (1)
• C
  92% (33)

Explanation

The deep learning renaissance is widely attributed to three converging factors: (1) Hardware advances - especially GPUs (and later TPUs) that dramatically accelerated matrix operations required by neural networks; (2) Large datasets - the internet age produced massive labeled datasets like ImageNet that gave models enough examples to learn rich representations; and (3) Algorithmic improvements - techniques such as better activation functions (ReLU), dropout regularization, batch normalization, and improved optimizers (Adam, RMSProp) made training deep networks practical. Option A incorrectly substitutes 'fast internet connections' for large datasets. Option B references the 'Bag of Words' model, which is a classical NLP technique unrelated to deep learning breakthroughs. Option D references smartphones and social media, which are enablers of data collection but not direct technical breakthroughs.

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