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
Topic: Natural Language Processing
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...
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...
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...
![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...
![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...
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...
![Tutorial TF-IDF vs Word2Vec For Text Classification [How To In Python With And Without CNN]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/02/word2vec-text-classification.jpg?fit=1024%2C576&ssl=1)
Tutorial TF-IDF vs Word2Vec For Text Classification [How To In Python With And Without CNN]
Word2Vec for text classification Word2Vec is a popular algorithm used for natural language processing and text classification. It is a neural network-based approach that learns distributed...
Deep Belief Network — Explanation, Application & How To Get Started In TensorFlow
How does the Deep Belief Network algorithm work? Common applications. Is it a supervised or unsupervised learning method? And how do they compare to CNNs? And how to create an implementation in...
Top 10 Natural Language Processing (NLP) Research Papers Worth Reading For Beginners
Reading research papers is integral to staying current and advancing in the field of NLP. Research papers are a way to share new ideas, discoveries, and innovations in NLP. They also give a more...
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...
Understanding Elman RNN — Uniqueness & How To Implement In Python With PyTorch
What is the Elman neural network? Elman Neural Network is a recurrent neural network (RNN) designed to capture and store contextual information in a hidden layer. Jeff Elman introduced it in 1990....
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...
Gated Recurrent Unit Explained & How They Compare LSTM, RNN & CNN
What is a Gated Recurrent Unit? A Gated Recurrent Unit (GRU) is a Recurrent Neural Network (RNN) architecture type. It is similar to a Long Short-Term Memory (LSTM) network but has fewer parameters...
![How To Use Text Normalization Techniques In NLP With Python [9 Ways]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/01/keyword-normalisation.png?fit=1024%2C576&ssl=1)
How To Use Text Normalization Techniques In NLP With Python [9 Ways]
Text normalization is a key step in natural language processing (NLP). It involves cleaning and preprocessing text data to make it consistent and usable for different NLP tasks. The process includes...
