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Large Neural Networks Benefit from Huge Data
Larger neural networks can obtain better performance, and the best performance comes from training a very large neural network while also having a huge amount of data.
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Supervised Learning
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Data Availability as a Driver of Deep Learning Progress
Computational Scale as a Driver of Deep Learning Progress
Older Learning Algorithms Can Plateau with More Data
Large Neural Networks Benefit from Huge Data
Small Data Regime Can Favor Hand-Engineered Features
According to Andrew Ng, what are the two biggest drivers of recent deep learning progress?
True or False: Neural network ideas are a brand-new invention of the last decade.
Two of the biggest drivers of recent progress have been data availability and _____.
Match each driver of deep learning progress to its correct description.
Order the reasoning steps explaining why deep learning is taking off now.
Explain why decades-old deep learning ideas are only now driving major progress.
Diagnose why a company's old algorithm and new neural network perform differently as data grows.
In one to two sentences, why might a small dataset favor hand-engineered features over a large neural network?
Which statement best reflects the relationship between deep learning ideas and their timing of impact?
True or False: Data availability and computational scale are described as the two biggest drivers of recent deep learning progress.
Learn After
Bigger Networks and More Data as Reliable Improvement Levers
One Million Examples Can Favor a Neural Network
Achieving best performance with neural networks
Impact of network size on performance
The two keys to _____ performance
Matching scale components to their effects
Steps to maximize neural network performance
Analyzing the relationship between model size and data volume
Scaling a speech recognition system
The two requirements for top performance
The effect of training larger neural networks
Data requirements for large networks