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Naina Chaturvedi

@NainaChaturved8

Dec 16, 2023

✅Attention Mechanism in Transformers- Explained in Simple terms.
A quick thread 👇🏻🧵
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ Attention mechanism calculates attention scores between all pairs of tokens in a sequence. These scores are then used to compute weighted representations of each token based on its relationship with other tokens in the sequence.

2/ This process generates context-aware representations for each token, allowing the model to consider both the token's own information and information from other tokens.

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Naina Chaturvedi

@NainaChaturved8

Nov 13, 2023

✅Regularization is a technique used in ML to prevent overfitting and improve the generalization of a model - Explained in Simple terms.
A quick thread 👇🏻🧵
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ Regularization is a technique in machine learning used to prevent overfitting by adding a penalty term to the model's loss function. The penalty discourages overly complex models and promotes simpler ones, improving generalization to new, unseen data.

2/ When to use regularization:

Use regularization when you suspect that your model is overfitting the training data.
Use it when dealing with high-dimensional datasets where the number of features is comparable to or greater than the number of samples.

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Naina Chaturvedi

@NainaChaturved8

Nov 9, 2023

✅XGBoost is a powerful and efficient gradient boosting library designed for ML tasks, specifically for supervised learning problems- Explained in Simple terms.
A quick thread 🧵👇🏻
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ XGBoost is ensemble learning method that combines multiple decision trees into a strong predictive model. It builds decision trees sequentially, where each tree corrects errors of previous ones. XGBoost optimizes a differentiable loss function to minimize prediction errors.

2/ When to Use XGBoost:

Use XGBoost when you need a highly accurate predictive model, especially in situations where other algorithms may struggle with complex patterns and relationships in the data.

Read 25 tweets

Naina Chaturvedi

@NainaChaturved8

Nov 9, 2023

✅Gradient Boosting is a powerful machine learning technique used for both regression and classification tasks - Explained in Simple terms.
A quick thread 🧵👇🏻
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ Gradient Boosting is an ensemble learning method that combines the predictions of multiple weak learners (often decision trees) to create a stronger and more accurate predictive model.

2/ How Gradient Boosting Works:

Gradient Boosting builds an ensemble of decision trees sequentially. It starts with a simple model (typically a single tree) and then iteratively adds more trees to correct the errors made by the previous ones.

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Naina Chaturvedi

@NainaChaturved8

Nov 6, 2023

✅Cross-validation in ML is particularly useful for estimating how well a model will perform on unseen data - Explained in Simple terms.
A quick thread 🧵👇🏻
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ Cross-validation involves splitting the dataset into multiple subsets and using different parts of the data for training and testing at each iteration. The primary goal of cross-validation is to obtain a more robust and unbiased estimate of a model's performance.

2/ Why use Cross Validation -
Performance Estimation: Cross-validation provides a more robust and unbiased estimate of a model's performance. It helps you to obtain a more accurate assessment of how well your model will perform on new, unseen data.

Read 25 tweets

Naina Chaturvedi

@NainaChaturved8

Oct 22, 2023

✅Feature selection and Feature scaling are crucial Feature Engineering steps - Explained in Simple terms.
A quick thread 👇🏻🧵
#MachineLearning #Coding #100DaysofCode #deeplearning #DataScience
PC : Research Gate

1/ Feature selection is the process of choosing a subset of the most relevant features (variables or columns) from your dataset. It involves excluding less informative or redundant features to improve model performance and reduce computational complexity.

2/ When to Use It:
High-Dimensional Data: Feature selection is crucial when you have a high-dimensional dataset, meaning there are many features compared to the number of data points. High dimensionality can lead to overfitting and increased computational costs.

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