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 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)
The Natural Language Processing (NLP) Blog
Topic: Machine Learning
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...
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...
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....
![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...
Fuzzy Logic Made Easy — Its Application In AI, Machine Learning & Natural Language Processing (NLP)
What is fuzzy logic? Fuzzy logic is a mathematical approach to reasoning about uncertain or vague information. Rather than the traditional binary true or false values found in classical logic, it is...
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...
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...
