hunch_net hunch_net-2013 hunch_net-2013-477 knowledge-graph by maker-knowledge-mining

477 hunch net-2013-01-01-Deep Learning 2012


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Introduction: 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition . Wide adoption of deep learning for speech recognition . Significant industry support . Gains in image recognition . This is a rare event in research: a significant capability breakout. Congratulations are definitely in order for those who managed to achieve it. At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data.


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1 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. [sent-1, score-1.128]

2 Signs of this include: Winning a Kaggle competition . [sent-2, score-0.136]

3 Wide adoption of deep learning for speech recognition . [sent-3, score-0.995]

4 This is a rare event in research: a significant capability breakout. [sent-6, score-0.702]

5 Congratulations are definitely in order for those who managed to achieve it. [sent-7, score-0.499]

6 At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data. [sent-8, score-1.751]


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Introduction: 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition . Wide adoption of deep learning for speech recognition . Significant industry support . Gains in image recognition . This is a rare event in research: a significant capability breakout. Congratulations are definitely in order for those who managed to achieve it. At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data.

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Introduction: 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition . Wide adoption of deep learning for speech recognition . Significant industry support . Gains in image recognition . This is a rare event in research: a significant capability breakout. Congratulations are definitely in order for those who managed to achieve it. At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data.

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