What is BERT in the context of NLP? In Natural Language Processing (NLP), the quest for models genuinely understanding and generating human language has been a longstanding challenge. One...
![The BERT Algorithm (NLP) Made Simple [Understand How Large Language Models (LLMs) Work]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2024/02/self-attention-example-1.jpg?fit=1200%2C675&ssl=1)
The Natural Language Processing (NLP) Blog
Topic: Neural Networks
Multilayer Perceptron Explained And How To Train & Optimise MLPs
What is a Multilayer perceptron (MLP)? In artificial intelligence and machine learning, the Multilayer Perceptron (MLP) stands as one of the foundational architectures, wielding remarkable...
Learning Rate In Machine Learning And Deep Learning Made Simple
Machine learning algorithms are at the core of many modern technological advancements, powering everything from recommendation systems to autonomous vehicles. Optimisation is central to the...
Link Prediction For Graph Neural Networks (GNN) Made Simple & 6 Powerful Tools
What is Link Prediction Based on Graph Neural Networks? Link prediction, a crucial aspect of network analysis, is the predictive compass guiding our understanding of complex relationships within...
![Self-Supervised Learning Made Simple [How To Train Powerful ML Models]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/12/pretext-tasks.jpg?fit=1200%2C675&ssl=1)
Self-Supervised Learning Made Simple [How To Train Powerful ML Models]
What is Self-Supervised Learning? Self-supervised learning (SSL) is a machine learning technique where a model learns representations or features directly from the input data without explicit...
![Machine Learning With Graphs Made Simple [& Practical How To Guide]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/12/nodes-edges-graph.jpg?fit=1200%2C675&ssl=1)
Machine Learning With Graphs Made Simple [& Practical How To Guide]
What is Machine Learning with Graphs? Machine learning with graphs refers to applying machine learning techniques and algorithms to analyze, model, and derive insights from graph-structured data. In...
Prototypical Networks Explained, Compared To Other Networks & How To Tutorial In PyTorch
What is a Prototypical Network? At its core, Prototypical Networks represent a groundbreaking approach to tackling the complexities of classification problems, especially in scenarios where labelled...
![Exploding Gradient Explained: How To Detect & Overcome It [6 Best Practices]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/12/exploding-gradient.jpg?fit=1200%2C675&ssl=1)
Exploding Gradient Explained: How To Detect & Overcome It [6 Best Practices]
What is the Exploding Gradient Problem? Neural networks optimize their parameters using gradient-based optimization algorithms like gradient descent. Gradients represent the slope of the loss...
Gradient Clipping Explained & Practical How To Guide In Python
What is Gradient Clipping in Machine Learning? Gradient clipping is used in deep learning models to prevent the exploding gradient problem during training. During the training process of neural...
Teacher Forcing In Recurrent Neural Networks (RNNs): An Advanced Concept Made Simple
What is teacher forcing? Teacher forcing is a training technique commonly used in machine learning, particularly in sequence-to-sequence models like Recurrent Neural Networks (RNNs) and...
Continual Learning Made Simple, How To Get Started & Top 4 Models
The need for continual learning In the ever-evolving landscape of machine learning and artificial intelligence, the ability to adapt and learn continuously (continual learning) has become...
Sequence-to-Sequence Architecture Made Easy & How To Tutorial In Python
What is sequence-to-sequence? Sequence-to-sequence (Seq2Seq) is a deep learning architecture used in natural language processing (NLP) and other sequence modelling tasks. It is designed to handle...
Cross-Entropy Loss — Crucial In Machine Learning — Complete Guide & How To Use It
What is cross-entropy loss? Cross-entropy Loss, often called "cross-entropy," is a loss function commonly used in machine learning and deep learning, particularly in classification tasks. It...
Natural Language Generation Explained & 2 How To Tutorials In Python
What is natural language generation? Natural Language Generation (NLG) is a subfield of artificial intelligence (AI) and natural language processing (NLP) that focuses on the automatic generation of...
Top 8 Loss Functions Made Simple & How To Implement Them In Python
What are loss functions? Loss functions, also known as a cost or objective functions, are critical component in training machine learning models. It quantifies a machine learning model's performance...
How To Implement Cross-lingual Transfer Learning In 5 Different Ways
What is cross-lingual transfer learning? Cross-lingual transfer learning is a machine learning technique that involves transferring knowledge or models from one language to another, typically to...
Understanding Dropout in Neural Network: Enhancing Robustness and Generalization
What is dropout in neural networks? Dropout is a regularization technique used in a neural network to prevent overfitting and enhance model generalization. Overfitting occurs when a neural network...
Graph Neural Network Explained & How To Tutorial In Python With PyTorch
Graph Neural Network (GNN) is revolutionizing the field of machine learning by enabling effective modelling and analysis of structured data. Originally designed for graph-based data, GNNs have found...
