Cross Validation Techniques
Published:
Cross validation is a method of estimating expected prediction error.
Posts by Tags
Data Science
Deep Learning
Don’t Be Fooled by Overconfident AI: Understanding Calibration in the Classification Task
Published:
Let’s consider an example of a deep learning model used for multi-class classification, such as a model that predicts the type of flower in an image. Suppose we have a dataset of 1000 images of flowers, each labeled with one of three possible classes: rose, daisy, or sunflower. We train a deep learning model to classify these images into one of these three classes.
How to Reproduce Pytorch Code?
Published:
We do not guarantee perfectly reproducible results across PyTorch releases or platforms. Additionally, results may not be reproducible between CPU and GPU runs, even with identical seeds.
Science in the age of large language models (LLMs)
Published:
Large language models (LLMs) are a type of artificial intelligence (AI) that can generate and understand text. They are trained on massive datasets of text and code and can be used for a variety of tasks, including writing, translating, and answering questions.
K-fold cross validation
Cross Validation Techniques
Published:
Cross validation is a method of estimating expected prediction error.
KAN
Is New Sheriff in Town? Kolmogorov-Arnold Networks (KANs) vs. Multi-Layer Perceptrons (MLPs)
Published:
Kolmogorov-Arnold Networks (KANs) present an exciting alternative to Multi-Layer Perceptrons (MLPs). Both KANs and MLPs have strong mathematical roots, but they diverge in their underlying principles. MLPs rely on the universal approximation theorem, while KANs are grounded in the Kolmogorov-Arnold representation theorem.
Kolmogorov–Arnold representation theorem
Is New Sheriff in Town? Kolmogorov-Arnold Networks (KANs) vs. Multi-Layer Perceptrons (MLPs)
Published:
Kolmogorov-Arnold Networks (KANs) present an exciting alternative to Multi-Layer Perceptrons (MLPs). Both KANs and MLPs have strong mathematical roots, but they diverge in their underlying principles. MLPs rely on the universal approximation theorem, while KANs are grounded in the Kolmogorov-Arnold representation theorem.
LLM
Science in the age of large language models (LLMs)
Published:
Large language models (LLMs) are a type of artificial intelligence (AI) that can generate and understand text. They are trained on massive datasets of text and code and can be used for a variety of tasks, including writing, translating, and answering questions.
MLP
Is New Sheriff in Town? Kolmogorov-Arnold Networks (KANs) vs. Multi-Layer Perceptrons (MLPs)
Published:
Kolmogorov-Arnold Networks (KANs) present an exciting alternative to Multi-Layer Perceptrons (MLPs). Both KANs and MLPs have strong mathematical roots, but they diverge in their underlying principles. MLPs rely on the universal approximation theorem, while KANs are grounded in the Kolmogorov-Arnold representation theorem.
Machine Learning
Cross Validation Techniques
Published:
Cross validation is a method of estimating expected prediction error.
Open-AI
Science in the age of large language models (LLMs)
Published:
Large language models (LLMs) are a type of artificial intelligence (AI) that can generate and understand text. They are trained on massive datasets of text and code and can be used for a variety of tasks, including writing, translating, and answering questions.
Pytorch
How to Reproduce Pytorch Code?
Published:
We do not guarantee perfectly reproducible results across PyTorch releases or platforms. Additionally, results may not be reproducible between CPU and GPU runs, even with identical seeds.
Reproducibility
How to Reproduce Pytorch Code?
Published:
We do not guarantee perfectly reproducible results across PyTorch releases or platforms. Additionally, results may not be reproducible between CPU and GPU runs, even with identical seeds.
calibration
Don’t Be Fooled by Overconfident AI: Understanding Calibration in the Classification Task
Published:
Let’s consider an example of a deep learning model used for multi-class classification, such as a model that predicts the type of flower in an image. Suppose we have a dataset of 1000 images of flowers, each labeled with one of three possible classes: rose, daisy, or sunflower. We train a deep learning model to classify these images into one of these three classes.
classification
Don’t Be Fooled by Overconfident AI: Understanding Calibration in the Classification Task
Published:
Let’s consider an example of a deep learning model used for multi-class classification, such as a model that predicts the type of flower in an image. Suppose we have a dataset of 1000 images of flowers, each labeled with one of three possible classes: rose, daisy, or sunflower. We train a deep learning model to classify these images into one of these three classes.
combinatorics
What is the Beggar’s Method in permutation and combinations?
Published:
Problems of kind “In how many ways we can distribute n identical coins among r Beggars?” in Permutation and combination is called beggars problem.
permutation
What is the Beggar’s Method in permutation and combinations?
Published:
Problems of kind “In how many ways we can distribute n identical coins among r Beggars?” in Permutation and combination is called beggars problem.
probability
What is the Beggar’s Method in permutation and combinations?
Published:
Problems of kind “In how many ways we can distribute n identical coins among r Beggars?” in Permutation and combination is called beggars problem.