supervised-learning | Predictions based on a set of training data | Machine Learning library
kandi X-RAY | supervised-learning Summary
kandi X-RAY | supervised-learning Summary
Supervised learning is the machine learning task of inferring a function from labeled training data. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Credits for some of the algorithms used go to Andrew Ng at Stanford University.
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Top functions reviewed by kandi - BETA
- Performs an predicted prediction
- Normalize a set of features
- Creates a new set of features
- Normalizes the predicted predictions of the predicted prediction
- calculate the species of the matrix
- raise an error if the feature is valid
- calculate the feature
- calculate cost cost
supervised-learning Key Features
supervised-learning Examples and Code Snippets
Lines of Code : 43
License : No License
Copyimport torch
from vit_pytorch import ViT, Dino
model = ViT(
image_size = 256,
patch_size = 32,
num_classes = 1000,
dim = 1024,
depth = 6,
heads = 8,
mlp_dim = 2048
)
learner = Dino(
model,
image_size = 256,
h # your training for-loop
for i, data in enumerate(dataloader):
optimizer.zero_grad()
embeddings = your_model(data)
augmented = your_model(your_augmentation(data))
labels = torch.arange(embeddings.size(0))
embeddings = torch.cat([embeddings, aug Community Discussions
Trending Discussions on supervised-learning
QUESTION
I've been working through the "Introduction to Machine Learning with Python" O'Reilly.
when running this block, I'm getting the convergencewarning.
...ANSWER
Answered 2021-Feb-12 at 11:25It means the data you are using is not in a good condition to converge with given algorithm. As you are using Logistic Regression, sklearn's default max_iter value is 100.
You can try to scale your data with StandartScaler. That's basically getting ready your data for the ML algorithm which can help converging.
Or you can change max_iter to see if it will converge or not.
For example:
QUESTION
On machinelearningmastery there is a post about how to create a supervised learning regression type dataset from one time series variable.
For example this:
...ANSWER
Answered 2020-Nov-17 at 13:12No, var1(t+1) would be the target and taken as y. The whole point is to predict the next step in the future from the current (and past) data.
QUESTION
I have a .txt file that contains data like this :
ANSWER
Answered 2020-Aug-08 at 13:29Alternativey, use
QUESTION
When Machine Learning is seen mathematically, we have cost functions, to reduce the error in the prediction for the next time and we keep on optimizing the parameters of the equation/s used in the particular algorithm.
I wonder where does this optimization happen in the library Sci-kit learn. There is no function for doing this job, so far I know,there are rather a bunch of algorithms as functions.
Can someone please tell me how do I optimize those parameters in sci-kit learn, and is there a way to do it in the mentioned library or is it just for learning purposes. I saw the code of library of logistic regression but got nothing.
Any effort is appreciated.
...ANSWER
Answered 2020-Feb-04 at 19:12I got it. GridsearchCV is the answer, thats what I was looking for. I think it allows us to choose the values of alpha, c and number of iterations, therefore, not allowing to alter the values of weights directly and I think thats ok or thats how we'd assign values to those parameters after carrying out the same process independtly. This article helped me to understand it well.
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