# Tag Info

### Difference between machine learning and artificial intelligence

The subject areas Artifical Intelligence and Machine Learning (plus Data Science) are loosely defined, such that it is hard to make strict statements about how they relate. In the general case, it ...
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### Difference between machine learning and artificial intelligence

Machine learning in layman terms is an algorithm that allows machines to identify patterns in data and then develop a model which can be used to predict unseen data. Artificial Intelligence is the ...
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### What does it mean when someone says "Most of the data science algorithms are optimization problems"

Most algorithms try to minimizes some objecive functions. For example, in linear regresssion, given $(x_i, y_i)$, we try to find $\hat{y}_i= \alpha_0 + \sum_{j=1}^d \alpha_j x_{i,j}$ and we we want it ...
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### Deriving backpropagation equations "natively" in tensor form

Notation matters! The problem starts from: Given $\nabla a_j^{(k+1)} = \frac{\partial E}{\partial a_j^{(k+1)}}$ I don't like your notation! it's wrong in fact, in standard mathematical notation. The ...

### Difference between machine learning and artificial intelligence

ML, by Tom M. Mitchell: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, ...

### Difference between machine learning and artificial intelligence

A good example of AI, but not machine learning is evolutionary computation. Here instead of learning from experience (as in Tom M. Mitchell's definition) we have genotype changing in each generation ...
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### Which neural network is better?

When you train a neural network, you usually use 3 sets: one for training, one for development, one for testing. Your training set is here for (obviously) training your model: the performance of your ...
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### Where does AI/ML theories come into play when nowadays the AI libraries already so powerful?

First "linear algebra, statistics, complicated optimization" are not ML theories, they are mathematics toolkits that guides specific ML algorithms designs and improvement at operation level. ...
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### Difference between machine learning and artificial intelligence

As the great Tom Mitchell has said in his book "Machine Learning is the ability to learn without being explicitly programmed." Machine learning algorithms are widely employed and are encountered ...
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### What would be the best way to impute data?

You can treat the missing feature as the target variable of a sub-problem and create a classifier (e.g., a linear model, SVM, etc) for it.
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### Situations where advanced theoretical knowledge of ML helped solve a real world problem?

At the end of the day, as an applied data scientist you have a bag of tools you can use to solve business problems. It's helpful to know what your tools are capable of, where each is useful, and also ...
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### Difference between machine learning and artificial intelligence

Let's take the total Turing test as an example. A computer is often said to be intelligent if it can pass the total Turing test. A computer passes the test if a human interrogator, after posing some ...
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### Is it possible for a neural net to score as high as a different form of supervised learning?

Generally speak, no. Deep learning models struggle to compete when it comes to tabular data. If we head over to kaggle were people compete to build the best model we find that usually the best ...
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### Multiple solutions with same minima in MLP with same weights

If one permutes the connections of the hidden layer ($d!$ ways to do that), and move and rename connections appropriately, then one effectively has the same MLP with the exact same minima, yet the ...
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### How to input a list into my model and not have it care about order

If the range of possible integers is small, encode the presence of each integer as a boolean column in a feature vector. Example with a value range of 0-5. ...
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### A question on realizable sample complexity

We want to prove: If H is PAC learnable, then $\forall \epsilon, \exists C, \forall m \geq m_2:=Clog(1/\epsilon)(m_1+1/\epsilon^2), E[L] \leq \epsilon \mbox{ (a)}$ where $m_1:=m(\epsilon/2,1/2)$ ...
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### Decentralized machine learning

Using federated learning the model training does not require the whole data to be present at a centralized server instead the model training is decentralized such that the model gets trained ...
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### Is the search space of Hyperparameters Continuous or Discrete?

Continuous means only you have continuous variables. It can be convex or concave. It might not even be differentiable. Gradient descent only applies to differentiable convex problems (or convex ...
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### What is the name of this statistical interaction?

You might want to look at conditional entropy, H(A|B) and H(B|A).
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