[Multiple choice]

Which of the following options is the key factor that led the return of neural networks?

Question

[Multiple choice]

Which of the following options is the key factor that led the return of neural networks?

BlackTom AI · Accepted Answer

When evaluating why neural networks regained prominence, the question asks to identify the key factor that led to their return.
Option 1: Deep learning algorithms. While advances in algorithms for deep learning are important, simply naming 'deep learning algorithms' does not pinpoint the pivotal development that spurred renewed success; it is more of a category than a single enabling factor.
Option 2: World Wide Web. The web dramatically increased data availability, which aided data-driven approaches, but this alone does not explain why neural networks specifically became prominent again, especially since many methods could leverage data without the same breakthrough.
Option 3: the back-propagation learning algorithm. This is the core mechanism that made training deep, multi-layer neural networks practical. Back-propagation provided an efficient way to compute gradients and update weights across many layers, enabling the successful optimization of complex networks that had been difficult to train previously.
Option 4: Dramatically increased Computational power (Cloud computing). Greater compute capacity certainly facilitated training larger networks and longer experiments, but without an effective training algorithm like back-propagation, the computational gains would not have unlocked the practical training of deep architectures.
Option 5: Big data (five Vs). Abundant data helps neural networks learn better representations, yet without a feasible learning rule to adjust millions of parameters, the presence of data alone would not trigger the revival.
In summary, while all of these factors contributed to the resurgence of neural networks in various ways, the most foundational turning point that enabled practical training of deep networks is the back-propagation learning algorithm, which allows efficient gradient-based optimization across many layers. The other options describe important supporting factors, but do not singularly explain the key enabling mechanism.

42172 Introduction to Artificial Intelligence - Spring 2025 Practice quiz - Module 1

[Multiple choice] Which of the following options is the key factor that led the return of neural networks?

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42172 Introduction to Artificial Intelligence - Spring 2025 Practice quiz - Module 1

[Multiple choice] Which of the following options is the key factor that led the return of neural networks?

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类似问题

以下哪一项*正确*描述了反向传播算法的步骤？ Which steps *correctly* describe the backpropagation algorithm?

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Question at position 2 What does backpropagation refer to in neural networks? Computing gradients for all parametersMapping inputs to outputsNormalizing dataUpdating weights in the forward direction

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In the connectionist model, the weights vary as a function of training, in which feedback as to the correctness or incorrectness leads to a mathematical adjustment of the weights. A commonly used training procedure for this weight adjustment is known as ___.

What is backpropagation in the context of training a language model?

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以下哪一项正确描述了反向传播算法的步骤？ Which steps correctly describe the backpropagation algorithm?