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Deep Learning & Artificial Neural Networks
Last Updated on September 3, 2019 Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s and early 2000s.
When discussing why now is the time that deep learning is taking off at ExtractConf 2015 in a talk titled “What data scientists should know about deep learning“, he commented: very large neural networks we can now have and …
performance just keeps getting better as you feed them more data He provides a nice cartoon of this in his slides: Finally, he is clear to point out that the benefits from deep learning that we are seeing in practice come from supervised learning. From the 2015 ExtractConf talk, he commented: almost all the value today of deep learning is through supervised learning or learning from labeled data Earlier at a talk to Stanford University titled “Deep Learning”
in 2014 he made a similar comment: one reason that deep learning has taken off like crazy is because it is fantastic at supervised learning Andrew often mentions that we should and will see more benefits coming from the unsupervised side of the tracks as the field matures to deal with the abundance of unlabeled data available.
He has given this talk a few times, and in a modified set of slides for the same talk, he highlights the scalability of neural networks indicating that results get better with more data and larger models, that in turn require more computation to train.
he commented: Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features An elaborated perspective of deep learning along these lines is provided in his 2009 technical report titled “Learning deep architectures for AI”
Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features.
kind of learning where the representation you form have several levels of abstraction, rather than a direct input to output Geoffrey Hinton is a pioneer in the field of artificial neural networks and co-published the first paper on the backpropagation algorithm for training multilayer perceptron networks.
Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.
In the same article, they make an interesting comment that meshes with Andrew Ng’s comment about the recent increase in compute power and access to large datasets that has unleashed the untapped capability of neural networks when used at larger scale.
In a talk to the Royal Society in 2016 titled “Deep Learning“, Geoff commented that Deep Belief Networks were the start of deep learning in 2006 and that the first successful application of this new wave of deep learning was to speech recognition in 2009 titled “Acoustic Modeling using Deep Belief Networks“, achieving state of the art results.
Jurgen Schmidhuber is the father of another popular algorithm that like MLPs and CNNs also scales with model size and dataset size and can be trained with backpropagation, but is instead tailored to learning sequence data, called the Long Short-Term Memory Network (LSTM), a type of recurrent neural network.
Notably, recent advances in deep neural networks, in which several layers of nodes are used to build up progressively more abstract representations of the data, have made it possible for artificial neural networks to learn concepts such as object categories directly from raw sensory data.
Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level.
Although early approaches published by Hinton and collaborators focus on greedy layerwise training and unsupervised methods like autoencoders, modern state-of-the-art deep learning is focused on training deep (many layered) neural network models using the backpropagation algorithm.
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