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...
The Natural Language Processing (NLP) Blog
Topic: Deep Learning
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...
Practical Guide To Doc2Vec & How To Tutorial In Python
In today's data-driven world, making sense of vast volumes of text data is paramount. Natural Language Processing (NLP) techniques are at the forefront of unlocking the insights hidden within text,...
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...
![Multi-class Classification Explained With 3 How To Python Tutorials [Scikit-Learn, PyTorch & Keras]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/multi-class-classification.jpg?fit=1200%2C675&ssl=1)
Multi-class Classification Explained With 3 How To Python Tutorials [Scikit-Learn, PyTorch & Keras]
What is multi-class classification in machine learning? Multi-class classification is a machine learning task that aims to assign input data points to one of several predefined classes or...
![Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/active-learning-try-fail.jpg?fit=1200%2C675&ssl=1)
Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]
What is active learning in machine learning? Active learning is a machine learning technique that involves iteratively selecting and labelling the most informative examples from an unlabeled dataset...
What Is A Large Language Model? Use Cases, Benefits, Limitations & What Does The Future Hold?
What is a large language model? A large language model (LLM) is a type of artificial intelligence (AI) trained on massive text and code datasets. This allows them to learn the patterns and...
![Zero-shot Classification [Top 6 Models, How To Tutorial In Python & Alternatives]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/zero-shot-classification.jpg?fit=1200%2C675&ssl=1)
Zero-shot Classification [Top 6 Models, How To Tutorial In Python & Alternatives]
What is zero-shot classification? Zero-shot classification is a machine learning approach in which a model can classify data into multiple classes without any specific training examples for those...
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...
Activation Function: Top 9 Most Popular Explained & When To Use Them
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...
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...
L1 And L2 Regularization Explained, When To Use Them & Practical How To Examples
L1 and L2 regularization are techniques commonly used in machine learning and statistical modelling to prevent overfitting and improve the generalization ability of a model. They are regularization...
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...
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...
