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A technique for evaluating the importance of a featureor component by temporarily removing it from a model. You thenretrain the model without that feature or component, and if the retrained modelperforms significantly worse, then the removed feature or component waslikely important.
For example, suppose you train aclassification modelon 10 features and achieve 88% precision on thetest set. To check the importanceof the first feature, you can retrain the model using only the nine otherfeatures. If the retrained model performs significantly worse (for instance,55% precision), then the removed feature was probably important. Conversely,if the retrained model performs equally well, then that feature was probablynot that important.
A/B testing usually compares a single metric on two techniques;for example, how does model accuracy compare for twotechniques? However, A/B testing can also compare any finite number ofmetrics.
Accelerator chips (or just accelerators, for short) can significantlyincrease the speed and efficiency of training and inference taskscompared to a general-purpose CPU. They are ideal for trainingneural networks and similar computationally intensive tasks.
Binary classification provides specific namesfor the different categories of correct predictions andincorrect predictions. So, the accuracy formula for binary classificationis as follows:
Although a valuable metric for some situations, accuracy is highlymisleading for others. Notably, accuracy is usually a poor metricfor evaluating classification models that processclass-imbalanced datasets.
For example, suppose snow falls only 25 days per century in a certainsubtropical city. Since days without snow (the negative class) vastlyoutnumber days with snow (the positive class), the snow dataset forthis city is class-imbalanced.Imagine a binary classificationmodel that is supposed to predict either snow or no snow each day butsimply predicts "no snow" every day.This model is highly accurate but has no predictive power.The following table summarizes the results for a century of predictions:
In a neural network, activation functions manipulate theweighted sum of all the inputs to aneuron. To calculate a weighted sum, the neuron adds upthe products of the relevant values and weights. For example, suppose therelevant input to a neuron consists of the following:
A training approach in which thealgorithm chooses some of the data it learns from. Active learningis particularly valuable when labeled examplesare scarce or expensive to obtain. Instead of blindly seeking a diverserange of labeled examples, an active learning algorithm selectively seeksthe particular range of examples it needs for learning.
A sophisticated gradient descent algorithm that rescales thegradients of each parameter, effectively giving each parameteran independent learning rate. For a full explanation, seethis AdaGrad paper.
More generally, an agent is software that autonomously plans and executes aseries of actions in pursuit of a goal, with the ability to adapt to changesin its environment. For example, LLM-based agents might use theLLM to generate a plan, rather than applying a reinforcement learning policy.
The process of identifying outliers. For example, if the meanfor a certain feature is 100 with a standard deviation of 10,then anomaly detection should flag a value of 200 as suspicious.
A non-human mechanism that demonstrates a broad range of problem solving,creativity, and adaptability. For example, a program demonstrating artificialgeneral intelligence could translate text, compose symphonies, and excel atgames that have not yet been invented.
A non-human program or model that can solve sophisticated tasks.For example, a program or model that translates text or a program or model thatidentifies diseases from radiologic images both exhibit artificial intelligence.
Formally, machine learning is a sub-field of artificialintelligence. However, in recent years, some organizations have begun using theterms artificial intelligence and machine learning interchangeably.
A mechanism used in a neural network that indicatesthe importance of a particular word or part of a word. Attention compressesthe amount of information a model needs to predict the next token/word.A typical attention mechanism might consist of aweighted sum over a set of inputs, where theweight for each input is computed by another part of theneural network.
A tactic for training a decision forest in which eachdecision tree considers only a random subset of possiblefeatures when learning the condition.Generally, a different subset of features is sampled for eachnode. In contrast, when training a decision treewithout attribute sampling, all possible features are considered for each node.
A number between 0.0 and 1.0 representing abinary classification model'sability to separate positive classes fromnegative classes.The closer the AUC is to 1.0, the better the model's ability to separateclasses from each other.
For example, the following illustration shows a classifier modelthat separates positive classes (green ovals) from negative classes(purple rectangles) perfectly. This unrealistically perfect model hasan AUC of 1.0:
AUC is the area of the gray region in the preceding illustration.In this unusual case, the area is simply the length of the gray region(1.0) multiplied by the width of the gray region (1.0). So, the productof 1.0 and 1.0 yields an AUC of exactly 1.0, which is the highest possibleAUC score.
A system that learns to extract the most important information from theinput. Autoencoders are a combination of an encoder anddecoder. Autoencoders rely on the following two-step process:
Autoencoders are trained end-to-end by having the decoder attempt toreconstruct the original input from the encoder's intermediate formatas closely as possible. Because the intermediate format is smaller(lower-dimensional) than the original format, the autoencoder is forcedto learn what information in the input is essential, and the output won'tbe perfectly identical to the input.
AutoML is useful for data scientists because it can save them time andeffort in developing machine learning pipelines and improve predictionaccuracy. It is also useful to non-experts, by making complicatedmachine learning tasks more accessible to them.
A model that infers a prediction based on its own previouspredictions. For example, auto-regressive language models predict the nexttoken based on the previously predicted tokens.All Transformer-basedlarge language models are auto-regressive.
In contrast, GAN-based image models are usually not auto-regressivesince they generate an image in a single forward-pass and not iteratively insteps. However, certain image generation models are auto-regressive becausethey generate an image in steps.
A metric for summarizing the performance of a ranked sequence of results.Average precision is calculated by taking the average of theprecision values for each relevant result (each result inthe ranked list where the recall increases relative to the previous result).
Neural networks often contain many neurons across many hidden layers.Each of those neurons contribute to the overall loss in different ways.Backpropagation determines whether to increase or decrease the weightsapplied to particular neurons.
The learning rate is a multiplier that controls thedegree to which each backward pass increases or decreases each weight.A large learning rate will increase or decrease each weight more than asmall learning rate.
In calculus terms, backpropagation implements thechain rule.from calculus. That is, backpropagation calculates thepartial derivative of the error withrespect to each parameter.
Each word is mapped to an index in a sparse vector, wherethe vector has an index for every word in the vocabulary. For example,the phrase the dog jumps is mapped into a feature vector with non-zerovalues at the three indexes corresponding to the words the, dog, andjumps. The non-zero value can be any of the following:
A model used as a reference point for comparing how well anothermodel (typically, a more complex one) is performing. For example, alogistic regression model might serve as agood baseline for a deep model.
Batch inference can leverage the parallelization features ofaccelerator chips. That is, multiple acceleratorscan simultaneously infer predictions on different batches of unlabeledexamples, dramatically increasing the number of inferences per second.
A probabilistic neural network that accounts foruncertainty in weights and outputs. A standard neural networkregression model typically predicts a scalar value;for example, a standard model predicts a house priceof 853,000. In contrast, a Bayesian neural network predicts a distribution ofvalues; for example, a Bayesian model predicts a house price of 853,000 witha standard deviation of 67,200.
A Bayesian neural network relies onBayes' Theoremto calculate uncertainties in weights and predictions. A Bayesian neuralnetwork can be useful when it is important to quantify uncertainty, such as inmodels related to pharmaceuticals. Bayesian neural networks can also helpprevent overfitting.
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