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Also, keep in mind that not just any augmentation of the loss function is a regularization. For example, you can add terms to a loss function that enforce constraints on the solution but do not prevent overfitting nor facilitate generalization.


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Regularization is not limited to methods like L1/L2 regularization which are specific versions of what you showed. Regularization is any technique that would prevent network from overfitting and help network to be more generalizable to unseen data. Some other techniques are Dropout, Early Stopping, Data Augmentation, limiting the capacity of network by ...


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Something that I personally use is Google Trends. This is a very useful tool for verifying the interest of a broad public on some subject. Results can even be refined to include region and/or time span. For instance, here you can see a comparison for the interest in Tensorflow, Keras and Pytorch over the past 12 months:


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The most popular theoretical framework in use currently, in the neuromorphic (brain-inspired) computing community is the Neural Engineering Framework (NEF). Neural Engineering by Chris Eliasmith and Charles Anderson explains the framework comprehensively. As a follow up to that, How to Build a Brain by Chris Eliasmith describes the more recent and more high-...


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This is the original Q-Learning paper by Watkins, though you may need to pay for access to this. This is the Nature paper that introduced the DQN.


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Use the benchmarked algorithms or research papers will be a good start. Addition to that use the open sourced Bert GPT 2 like architectures is a good start.


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Well, I would say, that purpose of Bayesian inference is not transfer learning, but uncertainty estimation. In case you have good feature extractor in the beginning, you can adjust small number of parameters, like few last layers to achieve good quality in few epochs. However, this is about adjusting the means of distributions over each weight. Concerning ...


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I would like to add "The Master Algorithm" by Pedro Domingos. I would say it's more philosophical but still provides high level discussions about differences between algorithms. He also has a sense of humor which makes it a lighter read.


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The famous book Artificial Intelligence: A Modern Approach (by Stuart Russell and Peter Norvig) covers all or most of the theoretical aspects of artificial intelligence (such as deep learning) and it also dedicates one chapter to the common philosophical topics that you mention.


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