What is the difference between AI, ML, NN and DL? [closed]
2020年11月18日 · $\begingroup$ A common example that illustrates DL as subset of NN is all the "last century" neural network methods - e.g. perceptron classifiers of 1950s-1960s were considered a ML method in the AI field back then, they definitely are a neural network, but they definitely are not a DL method, they are shallow networks with few or even just a single layer, …
ai - Difference between machine learning and artificial intelligence ...
2020年12月23日 · Difference between the two: A.I. is a very broad field of computer intelligence in which machine learning is one of the ways it gains the intelligence to predict outcomes. But AI also contains robotics, speech synthesis, computer vision and others. So if I were to draw a Venn diagram of artificial intelligence then machine learning would be a ...
terminology - What is the difference between explainable and ...
2020年3月24日 · O’Rourke says that explainable ML uses a black box model and explains it afterwards, whereas interpretable ML uses models that are no black boxes. Christoph Molnar says interpretable ML refers to the degree to which a human can understand the cause of a decision (of a model). He then uses interpretable ML and explainable ML interchangably.
machine learning - Interpretable vs. transparent ML algorithms
2021年8月8日 · In the this link, the author describes that the difference between model transparency and explainability (ie interpretability) lie in their approaches to solving the black box problem in AI. Transparency solves this by the use of simple models, such as linear regression, that are well-known and use easy to follow steps.
Difference between a target and a label in machine learning
2019年1月16日 · Label would be the actual price of the house (the correct value I have for Training). Target the output of my ML system (both for training and inference). With the training I try to get my targets close to my labels. However my targets will probably never be perfectly correct. Did I understand correctly? $\endgroup$ –
machine learning - Difference between a parameter and a weight?
2019年11月23日 · Basically, the difference is that hyper-parameters are chosen by the user prior to learning (and affect the learning phase), while the parameters are computed by the algorithm during the learning process.
What is the difference between maximum likelihood hypothesis …
2020年9月16日 · I am really confused by these two definitions, since I can't grasp what is the difference between the two. I have understood that the maximum likelihood hypothesis is the one that, given some observed data, finds the parameters of the distribution such that I am most likly to understand the data.
What's the principal difference between ANN,RNN,DNN and CNN?
2019年9月5日 · They are called shallow when they have only one hidden layer (i.e. one layer between input and output). They are called deep when hidden layers are more than one (what people implement most of the time).
Data scientist vs machine learning engineer
2018年2月20日 · A Data Scientist is expected to have a deep understanding of stats/math and ML/AI, and is often the person who creates the tools used by ML engineers. So a ML engineer is basically closer to a specialised software engineer and a DS is closer to a computational statistician. $\endgroup$ –
What are the differences between biased and unbiased learners?
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