Introduction Imagine a group of robots cleaning a warehouse, a swarm of drones surveying a disaster zone, or autonomous cars navigating through city traffic. In each of these scenarios, multiple...
The Natural Language Processing (NLP) Blog
Topic: Deep Learning
Structured Prediction In Machine Learning: What Is It & How To Do It
What is Structured Prediction? In traditional machine learning tasks like classification or regression a model predicts a single label or value for each input. For example, an image classifier might...
![Policy Gradient [Reinforcement Learning] Made Simple In An Elaborate Guide](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/11/q-learning-explained.jpg?fit=1200%2C675&ssl=1)
Policy Gradient [Reinforcement Learning] Made Simple In An Elaborate Guide
Introduction Reinforcement Learning (RL) is a powerful framework that enables agents to learn optimal behaviours through interaction with an environment. From mastering complex games like Go to...
![Deep Q-Learning [Reinforcement Learning] Explained & How To Example](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2025/05/q-learning-example.jpg?fit=1440%2C810&ssl=1)
Deep Q-Learning [Reinforcement Learning] Explained & How To Example
Imagine teaching a robot to navigate a maze or training an AI to master a video game without ever giving it explicit instructions—only rewarding it when it does something right. This is the essence...
Deepfake Made Simple, How It Work & Concerns
What is Deepfake? In an age where digital content shapes our daily lives, a new phenomenon is challenging our ability to trust what we see and hear: deepfakes. The term "deepfake" is a blend of...
Multi-Task Learning Made Simple & Popular Approaches Explained
What is Multi-Task Learning? Multi-TaskMulti-task learning (MTL) is a machine learning approach in which a single model is trained to solve multiple tasks simultaneously rather than learning each...
![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 BERT Algorithm (NLP) Made Simple [Understand How Large Language Models (LLMs) Work]
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...
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...
Variational Autoencoders (VAEs) Made Simple & How To TensorFlow Tutorial
What are Variational Autoencoders (VAEs)? Autoencoders are ingenious, unsupervised learning mechanisms capable of learning efficient data representations. However, traditional autoencoders often...
Embeddings from Language Models (ELMo): Contextual Embeddings A Powerful Shift In NLP
What are Embeddings from Language Models (ELMo)? ELMo, short for Embeddings from Language Models, revolutionized the landscape of NLP by introducing contextual embeddings, a paradigm shift from...
Data2vec Explained: The Fusion Of Text, Image & Audio To Create Powerful AI Systems
What is Data2vec? Meta AI has introduced Data2vec, a groundbreaking framework for self-supervised learning that transcends the barriers between different data modalities. Data2vec proposes a unified...
![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...
![Feature Extraction: Extensive Guide & 3 How To Tutorials [Python, CNN, BERT]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/11/feature-extraction-cats-dogs-example.jpg?fit=1200%2C675&ssl=1)
Feature Extraction: Extensive Guide & 3 How To Tutorials [Python, CNN, BERT]
What is Feature Extraction in Machine Learning? Feature extraction is a fundamental concept in data analysis and machine learning, serving as a crucial step in the process of transforming raw data...
Autoregressive (AR) Models Made Simple For Predictions & Deep Learning
What Are Autoregressive (AR) Models? Autoregressive (AR) models are statistical and time series models used to analyze and forecast data points based on their previous values. These models are...
