University of Michigan - Ann Arbor

AI Agents

Deep learning is taking off because as the amount of data and computational power increases, deep learning algorithms perform better than traditional machine learning methods. 

You can improve performance by increasing the scale of the NN or giving it more data. Increasing the scale of the NN often places greater strain on technological systems. The more accessible route is taking advantage of the increase in available data.

“Why is deep learning taking off?” 

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

https://machinelearning-blog.com/2017/11/03/erster-blogbeitrag/#:~:text=Getting%20a%20better%20accuracy%20with,or%20huge%20amounts%20of%20data.&text=The%20recent%20breakthroughs%20in%20the,use%20more%20and%20more%20data.

If the basic technical ideas behind Deep Learning are around for decades, why are they taking off today? 

https://towardsdatascience.com/how-do-you-know-you-have-enough-training-data-ad9b1fd679ee

Why deep learning is taking off source

https://medium.com/@shaoliang.jia/why-is-deep-learning-taking-off-8244175929e7

Deep learning and its relationship with data

The curse of dimensionality refers to the challenge where many machine learning problems become exceedingly difficult when the number of dimensions (features) in the data is high. Specifically, the number of possible configurations of variables increases exponentially with the number of dimensions. For a dataset with $$d$$ dimensions and $$v$$ values to be distinguished for each dimension, the space is divided into $$O(v^d)$$ regions, requiring an exponential number of examples to adequately cover all configurations.

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