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...
The Natural Language Processing (NLP) Blog
Machine Learning – Deep Learning – Data Science
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...
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...
Top 20 Most Powerful Large Language Models For NLP Tasks & Transfer Learning In 2024
Natural Language Processing (NLP) has become an essential area of research and development in Artificial Intelligence (AI) in recent years. NLP models have been designed to help computers...
![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...
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 Apply Transfer Learning To Large Language Models (LLMs) — Detailed Explanation & Tutorial To Fine Tune A GPT-3 model
What is transfer learning for large language models (LLMs)? Their Advantages, disadvantages, different models available and applications in various natural language processing (NLP) tasks. Followed...
![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...
Top 8 Most Useful Anomaly Detection Algorithms For Time Series And Common Libraries For Implementation
How does anomaly detection in time series work? What different algorithms are commonly used? How do they work, and what are the advantages and disadvantages of each method? Be able to choose the...
Feedforward Neural Networks Made Simple With Different Types Explained
How does a feedforward neural network work? What are the different variations? With a detailed explanation of a single-layer feedforward network and a multi-layer feedforward network. What is a...
![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...
Understanding Generative Adversarial Network With A How To Tutorial In TensorFlow And Python
What is a Generative Adversarial Network (GAN)? What are they used for? How do they work? And what different types are there? This article includes a tutorial on how to get started with GANs in...
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...
