#Data Sequencing

[playstationvii]

AI Data learning and Data management:

Learning and Integration of predictive algorithms for information subsequent subsets, data storage and advanced AI learning:

Sure, here are some of the algorithms I am using in my project:   Linear regression: This is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The equation for linear regression is as follows: y = β0 + β1x1 + β2x2 + … + βnxn where y is the dependent variable, x1, x2, …, xn are the independent variables, and β0, β1, β2, …, βn are the coefficients of the model.

Support Vector Machines (SVM): SVM is a supervised learning algorithm that can be used for both classification and regression tasks. The algorithm works by finding the hyperplane that best separates the data points in the dataset. The equation for SVM is as follows: y = f(x) = w^T x + b where w is the weight vector, x is the input vector, and b is the bias term.

Decision Trees: Decision trees are a type of supervised learning algorithm that can be used for classification and regression tasks. The algorithm works by splitting the dataset into subsets based on the values of the features, and then it creates a tree-like structure that maps the decisions that need to be made to reach the desired outcome. The equation for decision trees is as follows: y = f(x) = w1x1 + w2x2 + … + wnxn where y is the outcome, w1, w2, …, wn are the weights, and x1, x2, …, xn are the features.

Naive Bayes: Naive Bayes is a probabilistic classification algorithm that is based on Bayes' theorem. The algorithm works by assuming that the features are conditionally independent, which means that the value of one feature does not depend on the value of any other feature. The equation for Naive Bayes is as follows: P(y|x) = P(x|y)P(y)/P(x) where P(y|x) is the probability of y given x, P(x|y) is the probability of x given y, P(y) is the prior probability of y, and P(x) is the marginal probability of x.

K-Nearest Neighbors (KNN): KNN is a non-parametric classification algorithm that works by finding the k closest neighbors of a point in the dataset and using their labels to predict the label of the point. The equation for KNN is as follows: y = f(x) = 1/k * (w1y1 + w2y2 + … + wkyk) where y is the predicted label, k is the number of neighbors, w1, w2, …, wk are the weights, and y1, y2, …, yk are the labels of the k closest neighbors.

Random Forest: Random Forest is an ensemble learning algorithm that combines multiple decision trees to create a more accurate and robust model. The algorithm works by creating multiple decision trees, each with a random subset of the features, and then using the majority vote of the trees to make a prediction. The equation for Random Forest is as follows: y = f(x) = 1/n * (f1(x) + f2(x) + … + fn(x)) where y is the predicted label, n is the number of trees, and f1, f2, …, fn are the individual decision trees.

Neural Networks: Neural networks are a type of artificial intelligence that is inspired by the structure and function of the human brain. The algorithm works by using a network of interconnected nodes, or neurons, to process input data and make predictions. The equation for neural networks is as follows: y = f(x) = w1 * f(w2x1 + w3x2 + … + wnxn) + b where y is the predicted label, w1, w2, …, wn are the weights, x1, x2, …, xn are the input features, and b is the bias term.

[darkdragon768]

Looking up the different routes on the deltarune wiki and... yeeaa I would have never discovered the snowgrave route on my own.

[saintshigaraki]

the mutants i’ve spent 30 hours on in lab were a success !!!!

[luxraydyne]

idk it's such a very small thing but "maybe you killed her with renju." "don't be ridiculous!" is very cool very epic i think. credit to both voice actors bc i believe that, their whole past and future entanglements aside, hitomi just fucking despised date for a second there to be honest

[cmaidaartworkblog]

Once I had enough high-resolution climate data to work with, the final part of the Climate phase was the creation of maps with discrete climate zones, which I produced in both the Trewartha classification scheme, left, and the Köppen classification scheme, right.

The final phase of the Ayrum mapmaking project was to create realistic satellite style maps, which began with mapping out soil colors and the ground cover of vegetation generally and tree-analogues specifically.

Once I knew where the plants belonged, I then determined what colors they'd be in the conditions they're adapted for, as seen in the maps, and under seasonal variation, with the chart showing how plants with certain adaptations react to seasonal changes in those conditions.

In these gifs we see the ground plants and tree canopies changing colors as the Solstices and Equinoxes expose them to greater or lesser rainfall and harsher or milder temperatures than what they're adapted for. Neither of these gifs provide a true image of what the surface looks like from space, but rather of the in-person appearance of whatever plants may be present.

Finally, using the vegetation density maps as raster masks for the seasonal plant color maps, and layering those with the snow-and-ice maps over the soil color map, we now have a much truer image of Ayrum's surface as of its (Northern) Winter, Spring, Summer, and Autumn months.

[ribbitflings]

the absolute fucking letdown when youre excited af about something cool you found that pertains to your interests—thats like a gold mine or candy shop for you—and you try to share it with other people so they can enjoy it too, only to be met with disinterest or feeble attempts at feigning interest

[elation0]

A233380 vs A233381

[lokh]

man. not me finding out that my finger placement is all wrong even though im capable of touch typing up to 100 wpm

[liuisi]

THANK YOU NIH BLAST TOOL FOR SAVING MY LIFE???

[smol-blue-bird]

star trek generations is. uh. not that good

[felicitypdf]

modern day '"we tell ourselves stories in order to live" in the international journal of computer auditing

[championsofmyheart]

MY SEQUENCES

[cmaidaartworkblog]

The next phase of the Ayrum project was climate, which I'll introduce with the resource that made my work possible in the first place: these datasets created by Nikolai Lofving Hersfeldt, who runs WorldBuildingPasta and shared all of this with my client and me via Panoply. These were tremendously helpful and I wouldn't be able to achieve a fraction of the final detail without them.

My own adjustment to this data begins with Surface Temperature, which came down to correcting the coastlines (I mistakenly sent in a version of the elevation map that resulted in continental shelves appearing above sea level), refining the effects of elevation, and adding a color gradient.

I was then able to combine this data into Annual Minimum, Average, and Maximum temperature maps, seen above, which was pretty useful too.

One use for the Annual Average map, for example, was providing a baseline to compare each month's data against, seen in the sequence above.

And from the Annual Minimum and Maximum maps, I was able to create a map that presents the overall range of temperatures throughout the year, which does a good job of showing just how extreme the conditions are in higher latitudes and further inland.

[fieriframes]

[Embedded in the data I gave you was a call sequence.]

[1o1percentmilk]

i don't even want to take half my classes that im registered for autumn quarter

[sshbpodcast]

Episode 131: Rick Berman Is Made of Lies

TNG: "Gambit" and "Phantasms"

Season 7 of TNG leaves your hosts cold again this week, tho opinions are divided over what's worse. First up: something we might be seeing more of as Picard tussles with smugglers in "Gambit", parts 1 and 2. After that, more Data dreams may tie into a malfunctioning warp core in "Phantasms".

Also this week: Worf puns, a Voyager tangent, and the tables are turned on Gordo.

Timestamps: Gambit: 3:20; Phantasms: 42:59