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Explain the significance of end-to-end learning for speech recognition regarding output complexity.
Question: Discuss the accelerating trend in deep learning regarding end-to-end systems for tasks such as speech recognition. Explain the specific inputs and outputs used for speech recognition, and how this relates to the complexity of the output produced by the model.
Sample answer: The accelerating trend in deep learning involves training end-to-end systems using the right (input, output) labeled pairs to produce complex results. For speech recognition, an end-to-end system inputs an audio clip and directly outputs a transcription (a sentence). This demonstrates that end-to-end models can produce outputs that are much richer than just a single number.
Key points:
- System requires appropriate (input, output) labeled pairs.
- The input is an audio clip.
- The output is a transcription (a sentence).
- The system can learn to produce outputs that are richer than a single number.
Rubric: Score based on the identification of audio as the input, transcription as the output, and the explanation that the output is richer than a single number.
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Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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