By Neil Pasricha
From the writer of the number one foreign Bestseller "The e-book of Awesome" and the award-winning, multimillion-hit site 1000awesomethings.com comes...
EVEN extra of the little issues that make us smile each day!
Neil Pasricha is again with a suite of hundreds of thousands extra amazing issues that deserve occasion including:
* Coming again in your personal mattress after a protracted trip
* Sneaking more cost-effective sweet into the motion picture theater
* Stepping on dry crunchy leaves at the sidewalk
* Letting move of the gasoline pump completely so that you finish on a around number
* whilst a child falls asleep on you
* ultimately peeing after retaining it forever
* Pulling a weed and getting all of the roots with it
* Seeing previous humans conserving hands
* while your puppy notices you're in a foul temper and is derived to determine you
* while your windshield wipers fit the beat of the music you're listening to
* the sleek feeling in your enamel once you get your braces off
* while the hiccups stop
* riding from a coarse street onto a delicate one
* dog breath
* while the houseguest leaves
* while the individual you're assembly is even later than you are
* Getting the keys in your first apartment
There's even area so you might write your own remarkable issues within the again.
Because couldn't all of us use (even extra) AWESOME!?
Read Online or Download The Book of (Even More) Awesome PDF
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Additional info for The Book of (Even More) Awesome
1 Structured Output Prediction . . . . . . . . . . . . . . . . . . 2 Graph-Based Semi-Supervised CRFs . . . . . . . . . . . . . . . 3 Structured Prediction with Hidden Structure . . . . . . . . . . 4 Optimization for PoMEN . . . . . . . . . . . . . . . . . . . . . Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Asymptotic Analysis for Semi-Supervised Learning . . . . . . .
This Bayesian flavor helps mitigate problems caused by the first assumption in this section: that prediction under the model θ is a good estimate of the true label distribution. Finally, let us consider how density-weighted methods make a similar approximation, but mitigate the overly strong assumption that x is sufficiently representative of the input distribution. By rewriting the equation for information gain above, we get: U Hθ (Y |x(u) ) − φ (x) ≈ Pθ (y|x)Hθ+ x,y (Y |x(u) ) . y u=1 The expression inside the summand represents the expected reduction in entropy for each instance in the pool if x is the selected query.
In other words, if two data points are in the same cluster, then they are highly likely to have the same class label. An equivalent statement can be made in terms of the lowdensity separation principle which states that the decision boundary should pass through a region of low density and therefore avoid cutting a data cluster. • Manifold Assumption: The marginal PX is supported on a lowdimensional manifold embedded in a high-dimensional ambient space. The conditional distribution P (y|x) is smooth, as a function of x, with respect to this low-dimensional manifold structure.