What is few-shot learning? Few-shot learning is a machine learning technique that aims to train models to learn new tasks or recognise new classes of objects using only a small amount of labelled...
The Natural Language Processing (NLP) Blog
Topic: Tutorial
How To Combine Numerical & Text Features: 10 Ways In Machine Learning And Deep Learning
Why Combine Numerical Features And Text Features? Combining numerical and text features in machine learning models has become increasingly important in various applications, particularly natural...
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...
CountVectorizer Tutorial: How To Easily Turn Text Into Features For Any NLP Task
What is CountVectorizer in NLP? CountVectorizer is a text preprocessing technique commonly used in natural language processing (NLP) tasks for converting a collection of text documents into a...
F1 Score The Ultimate Guide: Formulas, Explanations, Examples, Advantages, Disadvantages, Alternatives & Python Code
The F1 score formula The F1 score is a metric commonly used to evaluate the performance of binary classification models. It is a measure of a model's accuracy, and it takes into account both...
Latent Dirichlet Allocation (LDA) Made Easy And Top 3 Ways To Implement In Python
Latent Dirichlet Allocation explained Latent Dirichlet Allocation (LDA) is a statistical model used for topic modelling in natural language processing. It is a generative probabilistic model that...
Fine-tuning GPT-3: How To Tutorial In Python With Hugging Face
What is GPT-3? GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language model developed by OpenAI, a leading artificial intelligence research organization. GPT-3 is a deep neural...
![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...
N-grams Made Simple & How To Implement In Python (NLTK)
In natural language processing, n-grams are a contiguous sequence of n items from a given sample of text or speech. These items can be characters, words, or other units of text, and they are used to...
![How To Implement Natural Language Processing (NLP) Feature Engineering In Python [8 Techniques]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/03/nlp-feature-engineering.jpg?fit=1200%2C675&ssl=1)
How To Implement Natural Language Processing (NLP) Feature Engineering In Python [8 Techniques]
Natural Language Processing (NLP) feature engineering involves transforming raw textual data into numerical features that can be input into machine learning models. Feature engineering is a crucial...
![How To Implement Data Augmentation In Python [Image & Text (NLP)]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/03/Ontwerp-zonder-titel-46-1-2-scaled.webp?fit=600%2C338&ssl=1)
How To Implement Data Augmentation In Python [Image & Text (NLP)]
Top 7 ways of implementing data augmentation for both images and text. With the top 3 libraries in Python to use for image processing and NLP. What is data augmentation? Data augmentation is a...
Autoencoder Made Easy — Variations, Applications, Tutorial in Python With TensorFlow
Autoencoder variations explained, common applications and their use in NLP, how to use them for anomaly detection and Python implementation in TensorFlow What is an autoencoder? An autoencoder is a...
![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...
Backpropagation Made Easy With Examples And How To In Python With Keras
Why is backpropagation important in neural networks? How does it work, how is it calculated, and where is it used? With a Python tutorial in Keras. Introduction to backpropagation in Machine...
How To Implement Logistic Regression Text Classification In Python With Scikit-learn and PyTorch
Text classification is a fundamental problem in natural language processing (NLP) that involves categorising text data into predefined classes or categories. It can be used in many real-world...
Restricted Boltzmann Machines Explained & How To Tutorial In Python
How are RBMs used in deep learning? Examples, applications and how it is used in collaborative filtering. With a step-by-step tutorial in Python. What are Restricted Boltzmann Machines? Restricted...
