What is grid search? Grid search is a hyperparameter tuning technique commonly used in machine learning to find a given model's best combination of hyperparameters. Hyperparameters are parameters...
![Practical Guide To Grid Search [How To In Python For SVM & Logistic Regression]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/grid-search.jpg?fit=960%2C540&ssl=1)
The Natural Language Processing (NLP) Blog
Topic: Optimisation
Understanding Dropout in Neural Network: Enhancing Robustness and Generalization
What is dropout in neural networks? Dropout is a regularization technique used in a neural network to prevent overfitting and enhance model generalization. Overfitting occurs when a neural network...
L1 And L2 Regularization Explained, When To Use Them & Practical How To Examples
L1 and L2 regularization are techniques commonly used in machine learning and statistical modelling to prevent overfitting and improve the generalization ability of a model. They are regularization...
Hyperparameter Tuning In Machine Learning And Deep Learning: Top 6 Ways & How To Tutorial
What is hyperparameter tuning in machine learning? Hyperparameter tuning is critical to machine learning and deep learning model development. Machine learning algorithms typically have specific...
Endogenous vs Exogenous Variables Explained With Examples & Why It’s Important For Machine Learning
Endogenous and exogenous variables are two important concepts. In machine learning, endogenous variables are the variables that are directly influenced by other variables within the system being...
![How To Guide To Bias-Variance Trade-Off [2 Examples In Python: Polynomial Regression & SVM]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/04/bias-varince-trade-off.jpg?fit=960%2C540&ssl=1)
How To Guide To Bias-Variance Trade-Off [2 Examples In Python: Polynomial Regression & SVM]
What are bias, variance and the bias-variance trade-off? The bias-variance trade-off is a fundamental concept in supervised machine learning that refers to the trade-off between the error due to...
Data Quality In Machine Learning – Explained, Issues, How To Fix Them & Python Tools
What is data quality in machine learning? Data quality is a critical aspect of machine learning (ML). The quality of the data used to train a ML model directly impacts the accuracy and effectiveness...
![Adam Optimizer Explained & How To Use In Python [Keras, PyTorch & TensorFlow]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/03/adam-optimizer.jpg?fit=960%2C540&ssl=1)
Adam Optimizer Explained & How To Use In Python [Keras, PyTorch & TensorFlow]
Explanation, advantages, disadvantages and alternatives of Adam optimizer with implementation examples in Keras, PyTorch & TensorFlow What is the Adam optimizer? The Adam optimizer is a popular...
![What Is Overfitting & Underfitting [How To Detect & Overcome In Python]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/02/overfitting-underfitting.jpg?fit=960%2C540&ssl=1)
What Is Overfitting & Underfitting [How To Detect & Overcome In Python]
Illustrated examples of overfitting and underfitting, as well as how to detect & overcome them Overfitting and underfitting are two common problems in machine learning where the model becomes...
SMOTE Oversampling & Tutorial On How To Implement In Python And R
How does the algorithm work? What are the disadvantages and alternatives? And how do we use it in machine learning? How does SMOTE work? SMOTE stands for Synthetic Minority Over-sampling Technique....
The Vanishing Gradient Problem, How To Detect & Overcome It
When does it occur? How can you recognise it? And how to adapt your network to avoid the vanishing gradient problem. What is the vanishing gradient problem? The vanishing gradient problem is a...
Self-attention Made Easy & How To Implement It In PyTorch
Self-attention is the reason transformers are so successful at many NLP tasks. Learn how they work, the different types, and how to implement them with PyTorch in Python. What is self-attention in...
The Curse Of Variability In Machine Learning And How To Overcome It
What is the curse of variability? The curse of variability refers to the idea that as the variability of a dataset increases, the difficulty of finding a good model that can accurately predict...
Top 6 Most Useful Attention Mechanism In NLP Explained And When To Use Them
Numerous tasks in natural language processing (NLP) depend heavily on an attention mechanism. When the data is being processed, they allow the model to focus on only certain input elements, such as...
