Pranesh
https://www.goodreads.com/praneshss
“Probability theory naturally comes into play in what we shall call situation 1: When the data-point can be considered to be generated by some randomizing device, for example when throwing dice, flipping coins, or randomly allocating an individual to a medical treatment using a pseudo-random-number generator, and then recording the outcomes of their treatment. But in practice we may be faced with situation 2: When a pre-existing data-point is chosen by a randomizing device, say when selecting people to take part in a survey. And much of the time our data arises from situation 3: When there is no randomness at all, but we act as if the data-point were in fact generated by some random process, for example in interpreting the birth weight of our friend’s baby.”
― The Art of Statistics: Learning from Data
― The Art of Statistics: Learning from Data
“For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.”
― The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
― The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
“It’s simple math, you know. Classic bell curve. Normal distribution. Most people are going to pile up in the middle, not going anywhere, but there’ll be a handful of outliers that go high and low, often for inexplicable reasons. Point is, even survivors don’t really understand why they survive—they just do.”
― Losing Mars
― Losing Mars
“Our search for the Master Algorithm is complicated, but also enlivened, by the rival schools of thought that exist within machine learning. The main ones are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has a set of core beliefs, and a particular problem that it cares most about. It has found a solution to that problem, based on ideas from its allied fields of science, and it has a master algorithm that embodies it.”
― The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
― The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
“Supervised learning algorithms typically require stationary features. The reason is that we need to map a previously unseen (unlabeled) observation to a collection of labeled examples, and infer from them the label of that new observation. If the features are not stationary, we cannot map the new observation to a large number of known examples. But stationary does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.”
― Advances in Financial Machine Learning
― Advances in Financial Machine Learning
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Pranesh’s 2025 Year in Books
Take a look at Pranesh’s Year in Books, including some fun facts about their reading.
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