Pragmatic Programming Techniques
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Pragmatic Programming Techniques
Pragmatic Programming Techniques In classical prediction use case, the predicted output is either a number (for regression) or category (for classification). A set of training data (x, y) where x i...
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Pragmatic Programming Techniques: August 2012
"Big Data Analytics" has recently been one of the hottest buzzwords. It is a combination of "Big Data" and "Deep Analysis". The former is a phenomenon of Web2.0 where a lot of transaction and user a...
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Pragmatic Programming Techniques: Compare Machine Learning models with ROC Curve
Pragmatic Programming Techniques Compare Machine Learning models with ROC Curve ROC Curve is a common method to compare performance between different models. It can also be used to pick trade-off ...
Horicky.blogspot.com news digest
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5 years
Structure Learning and Imitation Learning
In classical prediction use case, the predicted output is either a number (for regression) or category (for classification). A set of training data (x, y) where x is the input and y is the labeled output is provided to train a parameterized predictive...
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6 years
Reinforcement Learning Overview
There are basically 3 different types of Machine Learning
Supervised Learning: The major use case is Prediction. We provide a set of training data including the input and output, then train a model that can predict output from an unseen input.
Unsupervised Learning: The major use case is Pattern extraction. We provide a set of data that has no output, the algorithm will try to extract the underlying non-trivial structure within the data.... -
6 years
Regression model outputting probability density distribution
For a classification problem (let say output is one of the labels R, G, B), how do we predict ?
There are two formats that we can report our prediction
Output a single value which is most probable outcome. e.g. output "B" if P(B) > P(R) and P(B) > P(G)... -
6 years
AI is not a new term, it is multiple decades old starting around early 80s when computer scientist design algorithms that can "learn" and "mimic human behavior".
On the "learning" side, the most significant algorithm is Neural Network, which is not very successful due to overfitting (the model is too powerful but not enough data). Nevertheless, in some more specific tasks, the idea of "using data to fit a function...
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Web host: | Google LLC |
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Updated: | June 29, 2023 |
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