What is an activation function? In artificial neural networks, an activation function is a mathematical function that introduces non-linearity to the output of a neuron or a neural network layer. It...
The Natural Language Processing (NLP) Blog
Topic: Neural Networks
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...
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...
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...
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...
![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...
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...
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...
![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...
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 Implement Transformers For Natural Language Processing (NLP) [4 Python Tutorials]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/01/transformers.jpg?fit=1024%2C576&ssl=1)
How To Implement Transformers For Natural Language Processing (NLP) [4 Python Tutorials]
Transformers Implementations in TensorFlow, PyTorch, Hugging Face and OpenAI's GPT-3 What are transformers in natural language processing? Natural language processing (NLP) is a field of artificial...
Siamese Network In Natural Language Processing (NLP) Made Simple & How To Tutorial In Python
What is a Siamese network? It is also commonly known as one or few-shot learning. They are popular because less labelled data is required to train them. Siamese networks are often used to figure out...
Top 6 Most Useful Attention Mechanism In NLP Explained And When To Use Them
Numerous tasks in natural language processing (NLP) depend heavily on an attention mechanism. When the data is being processed, they allow the model to focus on only certain input elements, such as...
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 (NLP) technique. This powerful algorithm can learn and understand sequential data, making it ideal for analyzing text and...
