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1. Top results related to what is the difference between decent and descending functions

   • What is the difference between is.vector() and is.numeric() functions?

     Top Answer

     Answered Jan 26, 2020 · 1 votes

     is.numeric is a general test to check whether a vector is numeric or not. It will return TRUE only if the object passed to it is a vector and consists of only numeric data. Whereas, is.vector tests whether the object is a vector or not. It will return TRUE if the object passed is a vector.

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     1/5

   • What is the difference between the `A` and `W` functions in the Win32 API?

     Top Answer

     Answered Mar 19, 2020 · 97 votes

     The A functions use Ansi (not ASCII) strings as input and output, and the W functions use Unicode string instead (UCS-2 on NT4 and earlier, UTF-16 on W2K and later). Refer to MSDN for more details.

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     2/5

   • What is the difference between the coxph and cph functions for calculating Cox's proportional hazards model?

     Top Answer

     Answered Dec 23, 2013 · 4 votes

     RCS = restricted cubic spline. You can find the function's help file by looking at help(package="rms")

     Here's an excerpt of the source code, so you can see where the cph function calls the coxph.fit function (the guts of coxph in the survival package)

     ->cph

     Both cph and coxph give the same results as far as coefficients:

     ->library("survival")>library("rms")>>x = rbinom(100, 1,.5)>t = rweibull(100, 1, 1)>>m1 = coxph(Surv(t)~x)>m2 = cph(Surv(t)~x)>m1$coefficients x 0.2226732 >m2$coefficients x 0.2226732

     But you can see that the authors of the cph function have added some extra components to the results to fit their needs. Thus cph will be useful if you need one of those extra features, but otherwise, coxph will do just fine.

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     3/5

   • What is the difference between anonymous functions and partial functions?

     Top Answer

     Answered Aug 23, 2020 · 1 votes

     From the documentation on pattern matching anonymous functions that you linked:

     If the expected type is SAM-convertible to scala.Functionk[S1,…,Sk, R], the expression is taken to be equivalent to the anonymous function:

     (x1:S1,…,xk:Sk) => (x1,…,xk) match { case p1 => b1 … case pn => bn }

     Here, each xi is a fresh name. As was shown here, this anonymous function is in turn equivalent to the following instance creation expression, where T is the weak least upper bound of the types of all bi.

     new scala.Functionk[S1,…,Sk, T] { def apply(x1:S1,…,xk:Sk): T = (x1,…,xk) match { case p1 => b1 … case pn => bn } }

     If the expected type is scala.PartialFunction[S, R], the expression is taken to be equivalent to the following instance creation expression:

     new scala.PartialFunction[S, T] { def apply(x: S): T = x match { case p1 => b1 … case pn => bn } def isDefinedAt(x: S): Boolean = { case p1 => true … case pn => true case _ => false } }

     Your first code snippet is a pattern matching anonymous function, but not necessarily a partial function. It would only be turned into a PartialFunction if it was given to a method with a PartialFunction parameter or assigned to a variable of type PartialFunction.

     So you are right that just some (pattern matching) anonymous functions are partial functions (AFAIK, function literals defined with fat arrows, such as x => x, can only ever be used to create FunctionN instances and not PartialFunction instances).

     However, not all partial functions are anonymous functions. A sugar-free way to define PartialFunctions is extending the PartialFunction trait (which extends Function1) and manually overriding the isDefinedAt and apply methods. For example, divide2 could also be defined like this, with an anonymous class:

     val divide2 = new PartialFunction[Int, Int] { override def isDefinedAt(x: Int) = x != 0 override def apply(x: Int) = 42 / x}

     You probably won't see this very often, though, as it's a lot easier to just use pattern matching to define a PartialFunction.

     In the blog post by Alvin Alexander you linked, the author refers to the pattern matching anonymous partial function literal as an anonymous function only because it happens to be both a partial function and an anonymous function. You could also define the function like so:

     -List(42, "cat").collect(new PartialFunction[Any, Int] { def isDefinedAt(x: Any) = x.isInstanceOf[Int] def apply(x: Any) = x match { case i: Int => i + 1 }})

     It's no longer an anonymous function, although it's still an anonymous object that's the instance of an anonymous class. Or you could define a singleton object beforehand and then use that.

     object Foo extends PartialFunction[Any, Int] { def isDefinedAt(x: Any) = x.isInstanceOf[Int] def apply(x: Any) = x match { case i: Int => i + 1 }}List(42, "cat").collect(Foo)

     No matter how you define it, though, it's a partial function.

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     4/5

   • What is the difference between the random.choices() and random.sample() functions?

     Top Answer

     Answered Nov 29, 2022 · 61 votes

     The fundamental difference is that random.choices()

     will (eventually) draw elements at the same position (always sample from the entire sequence, so, once drawn, the elements are replaced - with replacement), while random.sample()

     will not (once elements are picked, they are removed from the population to sample, so, once drawn the elements are not replaced - without replacement).

     Note that here replaced (replacement) should be understood as placed back (placement back) and not as a synonym of substituted (and substitution).

     To better understand it, let's consider the following example:

     import randomrandom.seed(0)ll = list(range(10))print(random.sample(ll, 10))# [6, 9, 0, 2, 4, 3, 5, 1, 8, 7]print(random.choices(ll, k=10))# [5, 9, 5, 2, 7, 6, 2, 9, 9, 8]

     As you can see, random.sample() does not produce repeating elements, while random.choices() does.

     In your example, both methods have results with repeating values because you have repeating values in the original sequence, but, in the case of random.sample() those repeating values must come from different positions of the original input.

     Eventually, you cannot sample() more than the size of the input sequence, while this is not an issue with choices():

     # print(random.sample(ll, 20))# ValueError: Sample larger than population or is negativeprint(random.choices(ll, k=20))# [9, 3, 7, 8, 6, 4, 1, 4, 6, 9, 9, 4, 8, 2, 8, 5, 0, 7, 3, 8]

     A more generic and theoretical discussion of the sampling process can be found on Wikipedia.

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2. en.wikipedia.org › wiki › Gradient_descentGradient descent - Wikipedia

   en.wikipedia.org › wiki › Gradient_descent

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   Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function.

3. towardsdatascience.com › gradient-descentGradient Descent Algorithm — a deep dive | by Robert ...

   towardsdatascience.com › gradient-descent

   May 22, 2021 · Gradient descent (GD) is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML) and deep learning (DL) to minimise a cost/loss function (e.g. in a linear regression).

4. Videos

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     Gradient Descent in 3 minutes

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     Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation

     May 21, 2021

     150.8K Views

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     28:26

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     Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function

     Jul 21, 2018

     649K Views

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5. www.geeksforgeeks.org › gradient-descent-algorithmGradient Descent Algorithm in Machine Learning - GeeksforGeeks

   www.geeksforgeeks.org › gradient-descent-algorithm

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   • Gradient Descent in Machine Learning
   • How The Gradient Descent Algorithm Works
   • Gradient Descent Learning Rate
   • Vanishing and Exploding Gradients
   • Different Variants of Gradient Descent
   • Advantages & Disadvantages of Gradient Descent
   • Conclusion

   What is Gradient?

   A gradient is nothing but a derivative that defines the effects on outputs of the function with a little bit of variation in inputs.

   What is Gradient Descent?

   Gradient Descent stands as a cornerstone orchestrating the intricate dance of model optimization. At its core, it is a numerical optimization algorithm that aims to find the optimal parameters—weights and biases—of a neural network by minimizing a defined cost function. Gradient Descent (GD) is a widely used optimization algorithm in machine learning and deep learning that minimises the cost function of a neural network model during training. It works by iteratively adjusting the weights or p...

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   For the sake of complexity, we can write our loss function for the single row as below In the above function x and y are our input data i.e constant. To find the optimal value of weight w and bias b. we partially differentiate with respect to w and b. This is also said that we will find the gradient of loss function J(w,b) with respect to w and b t...

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   The learning rateis a critical hyperparameter in the context of gradient descent, influencing the size of steps taken during the optimization process to update the model parameters. Choosing an appropriate learning rate is crucial for efficient and effective model training. When the learning rate is too small, the optimization process progresses ve...

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   Vanishing and exploding gradientsare common problems that can occur during the training of deep neural networks. These problems can significantly slow down the training process or even prevent the network from learning altogether. The vanishing gradient problem occurs when gradients become too small during backpropagation. The weights of the networ...

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   There are several variants of gradient descent that differ in the way the step size or learning rate is chosen and the way the updates are made. Here are some popular variants:

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   Advantages of Gradient Descent

   1. Widely used:Gradient descent and its variants are widely used in machine learning and optimization problems because they are effective and easy to implement. 2. Convergence: Gradient descent and its variants can converge to a global minimum or a good local minimum of the cost function, depending on the problem and the variant used. 3. Scalability: Many variants of gradient descent can be parallelized and are scalable to large datasets and high-dimensional models. 4. Flexibility: Different...

   Disadvantages of gradient descent:

   1. Choice of learning rate:The choice of learning rate is crucial for the convergence of gradient descent and its variants. Choosing a learning rate that is too large can lead to oscillations or overshooting while choosing a learning rate that is too small can lead to slow convergence or getting stuck in local minima. 2. Sensitivity to initialization: Gradient descent and its variants can be sensitive to the initialization of the model’s parameters, which can affect the convergence and the qu...

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   In the intricate landscape of machine learning and deep learning, the journey of model optimization revolves around the foundational concept of gradient descent and its diverse variants. Through the lens of this powerful optimization algorithm, we explored the intricacies of minimizing the cost function, a pivotal task in training models.

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   • Video Duration: 3 min

6. medium.com › @fabrizioferrari › an-introduction-toAn Introduction to Gradient Descent: A Powerful ... - Medium

   medium.com › @fabrizioferrari › an-introduction-to

   Feb 12, 2023 · Introduction. Definition of gradient descent and why it is essential in machine learning. Gradient descent is an optimization algorithm that's used to find the values of parameters...

7. www.charlesbordet.com › en › gradient-descentGuide to Gradient Descent in 3 Steps and 12 Drawings

   www.charlesbordet.com › en › gradient-descent

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   Oct 19, 2018 · How does it work? What are the common pitfalls? The only prerequisite to this article is to know what a derivative is. What is Gradient Descent? It is an algorithm used to find the minimum of a function. That’s called an optimization problem and this one is huge in mathematics.

8. medium.com › nerd-for-tech › never-forget-gradientGradient Descent and Loss Function Simplified | Nerd For Tech

   medium.com › nerd-for-tech › never-forget-gradient

   Jul 18, 2021 · Gradient descent is the underlying principle by which any “learning” happens. We want to reduce the difference between the predicted value and the original value, also known as loss.

9. People also ask

   What is gradient descent in machine learning?

   • The Gradient Descent method lays the foundation for machine learning and deep learning techniques. Let’s explore how does it work, when to use it and how does it behave for various functions. 1. Introduction Gradient descent (GD) is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function.

   Gradient Descent Algorithm — a deep dive - Towards Data Science

   towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-cf04e8115f21

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   What is the difference between cost function and gradient descent?

   • The cost function evaluates the difference between the actual and predicted outputs. Gradient Descent is a fundamental optimization algorithm in machine learning used to minimize the cost or loss function during model training. It iteratively adjusts model parameters by moving in the direction of the steepest decrease in the cost function.

   Gradient Descent Algorithm in Machine Learning - GeeksforGeeks

   www.geeksforgeeks.org/gradient-descent-algorithm-and-its-variants/

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   Why is gradient descent not efficient?

   • It is not efficient enough when the number of parameters is too large, and sometimes we cannot solve for the first-order conditions easily if the function is too complicated. Thus, we usually minimize the cost function using the gradient descent algorithm for models with numerous features.

   Everything You Need to Know about Gradient Descent

   towardsdatascience.com/everything-you-need-to-know-about-gradient-descent-b1d9edfce0b5

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   What are the different types of gradient descent algorithms?

   • There are several types of gradient descent algorithms, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. Every kind of gradient descent algorithm has unique characteristics, and the applicable algorithm will depend on the specific requirements of the machine learning problem.

   An Introduction to Gradient Descent: A Powerful Optimization Algorithm

   medium.com/@fabrizioferrari/an-introduction-to-gradient-descent-a-powerful-optimization-algorithm-ff322ca264bf

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10. towardsdatascience.com › everything-you-need-toEverything You Need to Know about Gradient Descent

    towardsdatascience.com › everything-you-need-to

    Mar 8, 2021 · Gradient Descent is an algorithm that solves optimization problems using first-order iterations. Since it is designed to find the local minimum of a differential function, gradient descent is widely used in machine learning models to find the best parameters that minimize the model’s cost function.