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AI Maverick
AI Maverick

49 Followers

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2 days ago

Light Gradient Boosting Machine

Light GBM (Light Gradient Boosting Machine) is a popular open-source framework for gradient boosting. It is designed to handle large-scale datasets and performs faster than other popular gradient-boosting frameworks like XGBoost and CatBoost. Light GBM uses a gradient-based one-sided sampling method to split trees, which helps to reduce memory usage…

Lightgbm

6 min read

Light Gradient Boosting Machine
Light Gradient Boosting Machine
Lightgbm

6 min read


6 days ago

leave-out Vs KFold

In machine learning, a “splitter” refers to a tool or technique that is used to split a dataset into separate subsets for training and testing a machine learning model. Splitting a dataset is a common practice in machine learning to avoid overfitting the model to the training data and to…

Machine Learning

4 min read

leave-out Vs KFold
leave-out Vs KFold
Machine Learning

4 min read


6 days ago

Splitters in Machine learning

In machine learning, a splitter is a function or module used to split a dataset into two or more subsets for different purposes. Splitting a dataset is an essential step in many machine-learning tasks, such as model training, validation, and testing. The most common type of splitter is the train-test…

Machine Learning

4 min read

Splitters in Machine learning
Splitters in Machine learning
Machine Learning

4 min read


Mar 7

Multitask learning (MTL)

Multitask learning (MTL) is a machine learning approach that involves training a model to perform multiple tasks simultaneously using the same input features. In other words, instead of introducing a separate model for each task, MTL trains a single model that can perform multiple tasks. The idea behind MTL is…

Multi Task Learning

5 min read

Multitask learning (MTL)
Multitask learning (MTL)
Multi Task Learning

5 min read


Mar 1

Parameters Vs. Hyperparameter

Parameters In machine learning, a model parameter refers to a configuration variable that is internal to the model and is used to make predictions on new data. Model parameters are learned from training data, which is input to a machine learning algorithm. The algorithm uses the training data to adjust the…

Machine Learning

5 min read

Parameters Vs. Hyperparameter
Parameters Vs. Hyperparameter
Machine Learning

5 min read


Feb 25

Process of data in Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) and computer science that focuses on enabling computers to process, understand, and generate human language. …

NLP

3 min read

Process of data in Natural Language Processing (NLP)
Process of data in Natural Language Processing (NLP)
NLP

3 min read


Feb 24

BERT — Bidirectional Encoder Representations

BERT stands for Bidirectional Encoder Representations from Transformers. It is a state-of-the-art natural language processing (NLP) model developed by Google researchers in 2018. BERT uses a type of deep learning architecture called Transformers, which allows it to understand the context and meaning of words in a sentence or passage. …

Bert

5 min read

BERT — Bidirectional Encoder Representations
BERT — Bidirectional Encoder Representations
Bert

5 min read


Feb 22

Self-Attention in Machine learning

Self-attention is a mechanism used in machine learning, particularly in natural language processing (NLP), that allows a model to weigh the importance of different parts of an input sequence when making predictions or generating outputs. Introduction Self-attention is a mechanism that allows a neural network to selectively weigh the importance of…

Machine Learning

5 min read

Machine Learning

5 min read


Feb 16

All about loss functions in machine learning

In machine learning, a loss function is a mathematical function that measures how well a machine learning model is able to make predictions. The loss function compares the predicted output of the model to the true output and produces a score that indicates how different the two are. …

Machine Learning

7 min read

Machine Learning

7 min read


Feb 11

Introducing Rock-Paper- Scissors Dataset

An image dataset is a collection of digital images that are organized and labeled for use in machine learning and computer vision tasks. These datasets are used to train and test computer vision algorithms, such as object recognition, image classification, and segmentation. …

Machine Learning

5 min read

Machine Learning

5 min read

AI Maverick

AI Maverick

49 Followers

https://samanemami.github.io/

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