This article explains the factors that make large language models expensive.

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# Baeldung Author

## Enes Zvornicanin

Enes is a data scientist with over three years of experience, currently working as a freelancer for Toptal. Enes has a strong background in mathematics, computer science, and machine learning and is passionate about learning and exploring any area related to machine learning.

## Here's what I've written (so far):

## Baeldung on Computer Science

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- Machine Learning (19)
- Deep Learning (10)
- Artificial Intelligence (5)
- Math and Logic (4)
- Computer Vision (4)
- Algorithms (3)
- Data Science (2)
- Searching (1)

### Comparative Analysis of Top Large Language Models

Filed under Deep Learning

A quick and practical comparison of top LLMs.

### How Can We Detect Blocks of Text From Scanned Images?

Filed under Computer Vision

A quick and practical guide to text detection in images.

### Comparison Between BERT and GPT-3 Architectures

Filed under Deep Learning

A quick and practical comparison of BERT and GPT-3 architectures.

### Why Does ChatGPT Not Give the Answer All at Once?

Filed under Artificial Intelligence

A quick and practical explanation of why ChatGPT doesn’t give the answer all at once.

### What Are the Advantages of Kernel PCA Over Standard PCA?

Filed under Machine Learning

A quick and practical guide to advantages of Kernel PCA.

### Data Quality Explained

Filed under Data Science, Machine Learning

Learn the concept of ‘data’, its significance for businesses, and explore methods for assessing data quality.

### Why Is ChatGPT Bad at Math?

Filed under Artificial Intelligence

A quick and practical explanation why ChatGPT is bad at math.

### How Does ChatGPT Work?

Filed under Artificial Intelligence

A comprehensive introduction to ChatGPT internals.

### What Is and Why Use Temperature in Softmax?

Filed under Deep Learning, Machine Learning

A quick and practical guide to using temperature in Softmax.

### How Does a Neural Network Recognize Images?

Filed under Computer Vision

A quick and practical guide to how neural networks recognise images?

### Automated Machine Learning Explained

Filed under Machine Learning

Understand how automated machine learning works.

### Co-occurrence Matrices and Their Uses in NLP

Filed under Machine Learning

Learn more about Co-occurrence Matrices in NLP.

### What Is Independent Component Analysis (ICA)?

Filed under Machine Learning

A quick and practical guide to independent component analysis (ICA)

### What Are Embedding Layers in Neural Networks?

Filed under Deep Learning

A quick and practical guide to embedding layers in neural networks and their applications.

### Introduction to Gibbs Sampling

Filed under Data Science, Machine Learning, Math and Logic

A quick and practical introduction to Gibbs sampling.

### Differences Between Transfer Learning and Meta-Learning

Filed under Machine Learning

Learn about the concepts of transfer learning and meta-learning.

### What Does Backbone Mean in Neural Networks?

Filed under Deep Learning, Machine Learning

A quick an practical guide to backbones in neural networks.

### Getting the Closest String Match

Filed under Algorithms

A quick and practical guide to finding the closest string matches.

### What Is Feature Importance in Machine Learning?

Filed under Machine Learning

A guide to feature importance in Machine Learning.

### Differences Between Porter and Lancaster Stemming Algorithms

Filed under Algorithms

A comparison between the Porter and Lancaster stemming algorithms.

### What Exactly Is an N-Gram?

Filed under Deep Learning

Learn about n-grams and some practical applications for them.

### What Is YOLO Algorithm?

Filed under Computer Vision

A guide to the YOLO algorithm for object detention

### Bias Update in Neural Network Backpropagation

Filed under Deep Learning

Learn how to update the bias term with backpropagation.

### Convolutional Neural Network vs. Regular Neural Network

Filed under Deep Learning

A comparison between regular neural networks and convolutional neural networks.

### What Is Depth in a Convolutional Neural Network?

Filed under Artificial Intelligence, Math and Logic

Understand the term “depth” when it comes to convolutional neural networks.

### How to Use K-Fold Cross-Validation in a Neural Network?

Filed under Machine Learning

A guide to validating neural networks with K-Fold Cross-Validation.

### Differences Between Bidirectional and Unidirectional LSTM

Filed under Machine Learning

Understand the differences between bidirectional and unidirectional LSTM.

### Relation Between Learning Rate and Batch Size

Filed under Deep Learning, Machine Learning

An overview of the learning rate and batch size neural network hyperparameters

### Word2vec Word Embedding Operations: Add, Concatenate or Average Word Vectors?

Filed under Deep Learning, Machine Learning

An overview of the word2vec algorithm and the logic behind word embeddings.

### Accuracy vs AUC in Machine Learning

Filed under Machine Learning

Learn about two commonly used machine learning metrics, accuracy and AUC.

### When Coherence Score Is Good or Bad in Topic Modeling?

Filed under Machine Learning

Learn about Latent Dirichlet Allocation and the coherence score

### How to Calculate the Regularization Parameter in Linear Regression

Filed under Machine Learning

Go over an overview of linear regression, and why we need regularization.

### Difference Between a SVM and a Perceptron

Filed under Machine Learning

A quick and practical comparison between SVM and a perceptron.

### How to Design Deep Convolutional Neural Networks?

Filed under Computer Vision

Learn the basic concepts behind convolutional neural networks, commonly used in computer vision tasks, and how to construct them.

### Finding Dates, Times and Addresses in Emails

Filed under Searching

A quick and practical guide to extracting dates, times, and addresses from any text data.

### Choosing the best q and p from ACF and PACF plots in ARMA-type modeling

Filed under Math and Logic

Explore some important terms relared to time-series forecasting.

### Out-of-bag Error in Random Forests

Filed under Machine Learning

A quick and practical explanation of out-of-bag errors in random forests.

### Grey Wolf Optimization Algorithm

Filed under Algorithms, Math and Logic

Learn about the Grey Wolf Optimization (GWO) algorithm and how it works.