AI News, What is the Difference Between Deep Learning and “Regular” Machine Learning?
- On Sunday, June 3, 2018
- By Read More
What is the Difference Between Deep Learning and “Regular” Machine Learning?
The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters.
For example, think of a log-sigmoid unit in our network as a logistic regression unit that returns continuous values outputs in the range 0-1.
During training, we then use the popular backpropagation algorithm (think of it as reverse-mode auto-differentiation) to propagate the 'errors' from right to left and calculate the partial derivatives with respect to each weight to take a step into the opposite direction of the cost (or 'error') gradient. Now, the problem with deep neural networks is the so-called 'vanishing gradient' -- the more layers we add, the harder it becomes to 'update' our weights because the signal becomes weaker and weaker.
Of course, there must be sufficient discriminatory information in our dataset, however, the performance of machine learning algorithms can suffer substantially when the information is buried in meaningless features.
it's about algorithms that do the feature engineering for us to provide deep neural network structures with meaningful information so that it can learn more effectively. We can think of deep learning as algorithms for automatic 'feature engineering,' or we could simply call them 'feature detectors,' which help us to overcome the vanishing gradient challenge and facilitate the learning in neural networks with many layers.
The idea is that if a feature detector is useful in one part of the imagine it is likely that it is useful somewhere else, but at the same time it allows each patch of image to be represented in several ways.
Via the convolutional layers we aim to extract the useful features from the images, and via the pooling layers, we aim to make the features somewhat equivariant to scale and translation.
- On Wednesday, September 18, 2019
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