How to do anomaly detection in time series? What different algorithms are commonly used? How do they work, and what...
The Natural Language Processing (NLP) Blog
Machine Learning – Deep Learning – Data Science
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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...
How To Guide For Data Augmentation In Machine Learning In Python For Images & Text (NLP)
Top 7 ways of implementing data augmentation for both images and text. With the top 3 libraries in Python to use for...
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...
Adam Optimizer Explained & How To Implement In Top 3 Libraries
Explanation, advantages, disadvantages and alternatives of Adam optimizer with implementation examples in Keras,...
Backpropagation Made Easy With Examples And How To In Keras
Why is backpropagation important in neural networks? How does it work, how is it calculated, and where is it used?...
Restricted Boltzmann Machines Explained & How To Tutorial
How are RBMs used in deep learning? Examples, applications and how it is used in collaborative filtering. With a...
Fuzzy Logic Made Easy — Its Application In AI & Machine Learning
Where is fuzzy logic used? What standard algorithms are used, and how is it useful in AI/machine learning and natural...
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....
Understanding Elman RNN — Uniqueness & How To Implement
What is the Elman neural network? Elman Neural Network is a recurrent neural network (RNN) designed to capture and...
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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...
How To Implement Different Question-Answering Systems In NLP
Question answering (QA) is a field of natural language processing (NLP) and artificial intelligence (AI) that aims to...
Different Attention Mechanism In NLP Made Easy
Numerous tasks in natural language processing (NLP) depend heavily on an attention mechanism. When the data is being...
Why And How To Use LSTM In NLP Tasks
With a text classification example using Keras Long Short-Term Memory (LSTM) is a powerful natural language processing...
Advanced NLP Made Easy – How To Get Started With RNN
Elman RNNs, Long short-term memory (LSTM) networks, Gated recurrent units (GRUs), Bi-directional RNNs and Transformer...
Encoder Decoder Neural Network Simplified — When To Use Them
Encoder, decoder and encoder-decoder transformers are a type of neural network currently at the bleeding edge in NLP....
Most Popular Deep Learning For Natural Language Processing
What is deep learning for natural language processing? Deep learning is a part of machine learning based on how the...
Reinforcement Learning And Its Application In NLP
This article covers reinforcement learning and its application in natural language processing (NLP). It also covered...
