Pattern Pursuit (Main) - 30.07.2025

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An engaging unscramble letters challenge where participants decode jumbled terms related to machine learning, algorithms, and data science. Test your knowledge and quick thinking as you race to reveal hidden vocabulary and claim victory!

Creada por

Rajitvel Perumal D

Estados Unidos

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Pattern Pursuit - 30.07.2025

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Pattern Pursuit (Main) - 30.07.2025


Ordenar Letras Pattern Pursuit (Main) - 30.07.2025Versión en línea An engaging unscramble letters challenge where participants decode jumbled terms related to machine learning, algorithms, and data science. Test your knowledge and quick thinking as you race to reveal hidden vocabulary and claim victory! por Rajitvel Perumal D 1 Learning through labelled dataset. E N G E R N S I R I S E V U P L D A 2 Learning through unlabelled datasets. G N R S U A V U P E N L R I E S D N E I 3 Full form of AI L N T A G E L I F C C I E R L I E T A N I I 4 Collection of features and values as rows and columns. D T A T A E S 5 Program trained on data to perform certain tasks like prediction. E M L O D 6 Step by step procedure. M L O H T A I R G 7 Method for finding the best set of parameters M I I E Y Z A O A A O B N N I T S T I P 8 Transforms raw data into a more effective set of inputs E U F E E I T R I E N E N A G G N R 9 Process of optimizing the settings of a machine learning model before training begins. E P M N T Y I G R E R N P H T R U E A A 10 Type of artificial neural network primarily used for analyzing visual data like images and videos. E O U A N N R L L C I W V O T R T L E K O N U O N A 11 Type of RNN architecture designed to address the vanishing gradient problem. R E M T N O O H L M M R E O Y S G T R 12 Making AI systems more understandable and transparent to humans. I L L E E X P A N A A B I 13 Process of assessing the performance and generalizability of a machine learning model. A T L I O N D I A V 14 Vector that represents the direction and magnitude of the steepest ascent of a function at a given point. R A N D E G T I 15 Short form of "Bi-Directional Encoder Representations from Transformers" B R E T 16 Type of neural network used in unsupervised learning to learn efficient data codings. R E C E D T A O U N O 17 Learning technique used to categorize data into predefined classes. C N L I T S C O I F A S A I 18 Learning technique used to model and predict a continuous-valued output (dependent variable) based on the relationship with one or more input variables. N E E S S O R R G I 19 ( (2 * Recall * Precision) / (Precision + Recall) ) E C R O F S 1 20 Table used to evaluate the performance of a classification model. R M C I S N F T A U X O I O N 21 Neural network architecture used for performing machine learning tasks particularly in natural language processing (NLP) and computer vision. R R R O A T F N S E M 22 Deep learning model that is trained to process and convert a sequential data input into a specific sequential data output. L K R N U E N T R C R T W R R E E O E A U N 23 (TP + TN) / (TP + TN + FP + FN) A C C R U Y A C 24 A sequence of interactions between an agent and its environment, starting from an initial state and ending at a terminal state. I S E O P D E 25 One of the activation functions - Outputs into a range between 0 and 1 O F X A S T M 26 There is low data and insufficient learning. Model is having low accuracy. E N T G T U I N F R I D 27 The model learnt the training data too much which includes noise as well. Model is having high accuracy. G F V T I R N O E I T 28 Dataset used to train machine learning models. I G I N R N A T 29 Dataset used to test machine learning models. (Unseen data) I N G E T S T 30 Complete pass through the entire dataset P C E H O 31 The task of grouping data points based on their similarity with each other. N R U G T E L S I C 32 Technique used to prevent overfitting. A N R G O I U A I L R T E Z 33 Technique used to prevent overfitting - Also known as L1 and L2 Regularization. G R E O S I D S L A 34 Method to update weights and biases so as the neural network makes better predictions. N A I A C B A T G O P O K R P 35 Regularization technique used to prevent overfitting in neural networks. It works by randomly deactivating neurons during training. D P O T R U O 36 Model's sensitivity to fluctuations in the training data. V A R E C I N A 37 Algorithm for choosing the actions that address the exploration-exploitation dilemma. A L I H P P S N M O N G S O M T 38 This is used to maximize the future rewards rather than immediate rewards. R D F T N T S O C C I A O U 39 The change from one state to another after taking an action. S I N T O R A T I N 40 Returns a value of a state or state-action pair. N O C U F I A L E T N U V

Pattern Pursuit (Main) - 30.07.2025

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Pattern Pursuit (Main) - 30.07.2025Versión en línea

por Rajitvel Perumal D

Learning through labelled dataset.

Learning through unlabelled datasets.

Full form of AI

Collection of features and values as rows and columns.

Program trained on data to perform certain tasks like prediction.

Step by step procedure.

Method for finding the best set of parameters

Transforms raw data into a more effective set of inputs

Process of optimizing the settings of a machine learning model before training begins.

Type of artificial neural network primarily used for analyzing visual data like images and videos.

Type of RNN architecture designed to address the vanishing gradient problem.

Making AI systems more understandable and transparent to humans.

Process of assessing the performance and generalizability of a machine learning model.

Vector that represents the direction and magnitude of the steepest ascent of a function at a given point.

Short form of "Bi-Directional Encoder Representations from Transformers"

Type of neural network used in unsupervised learning to learn efficient data codings.

Learning technique used to categorize data into predefined classes.

Learning technique used to model and predict a continuous-valued output (dependent variable) based on the relationship with one or more input variables.

( (2 * Recall * Precision) / (Precision + Recall) )

Table used to evaluate the performance of a classification model.

Neural network architecture used for performing machine learning tasks particularly in natural language processing (NLP) and computer vision.

Deep learning model that is trained to process and convert a sequential data input into a specific sequential data output.

(TP + TN) / (TP + TN + FP + FN)

A sequence of interactions between an agent and its environment, starting from an initial state and ending at a terminal state.

One of the activation functions - Outputs into a range between 0 and 1

There is low data and insufficient learning. Model is having low accuracy.

The model learnt the training data too much which includes noise as well. Model is having high accuracy.

Dataset used to train machine learning models.

Dataset used to test machine learning models. (Unseen data)

Complete pass through the entire dataset

The task of grouping data points based on their similarity with each other.

Technique used to prevent overfitting.

Technique used to prevent overfitting - Also known as L1 and L2 Regularization.

Method to update weights and biases so as the neural network makes better predictions.

Regularization technique used to prevent overfitting in neural networks. It works by randomly deactivating neurons during training.

Model's sensitivity to fluctuations in the training data.

Algorithm for choosing the actions that address the exploration-exploitation dilemma.

This is used to maximize the future rewards rather than immediate rewards.

The change from one state to another after taking an action.

Returns a value of a state or state-action pair.