Explore how to calculate the average of a set of circular data.
Gabriele De Luca
Gabriele specializes in artificial intelligence and innovation and on the impact of technology on society. He has authored several scientific papers in the sectors of machine learning, natural language processing, network theory, and multi-agent simulations. On Baeldung he contributes to the section on computer science, where he publishes articles on the theory behind machine learning and artificial intelligence.
Here's what I've written (so far):
Baeldung on Computer Science
- Machine Learning (17)
- Math and Logic (15)
- Deep Learning (9)
- Algorithms (9)
- Artificial Intelligence (7)
- Programming (6)
- Latex (5)
- Core Concepts (3)
- Security (2)
- Graphs (2)
- Sorting (1)
- OS (1)
- Networking (1)
- Graph Theory (1)
- Data Structures (1)
- Data Science (1)
Explore the differences and the similarities between finite-state machines and Markov chains.
Explore the differences between strong-AI and weak-AI.
Explore the basic concepts of machine learning.
Explore the methods for preventing selection bias when we conduct statistical analysis.
Explore the most common techniques for feature selection and reduction for text classification.
Explore how to inflate or deflate a polygon utilizing homothety and offsetting.
Explore the components, representations, and applications of finite-state machines.
Explore the relationship between the number of support vectors and the performances of a support vector classifier.
Learn a conceptual difference between methods and functions.
Explore the Ackermann function and the problems associated with its computation.
Explore numeral systems and their associated concepts.
Learn about the ugly duckling theorem in its relationship with algorithmic bias.
Learn how to normalize the features of a table or dataset.
Learn about the Haversine formula for calculating great circle distances in spherical surfaces.
Explore an algorithm for placing nice gridlines on a bar chart.
Explore the difference between Omega notation for lower bounds and the Theta notation for tight bounds.
Study the roulette wheel selection method for genetic algorithms.
Learn how to determine whether a point is inside a polygon or not.
Learn how to determine a nice scale for the Y axis in a chart.
Explore how to generate a pseudorandom variable that’s distributed normally.
Compare gradient descent and Newton’s method for finding the minima in a cost function.
Learn how the “Did you mean?” algorithm works in Google.
Study sorting algorithms that are even worse than Bogosort.
Explore the definition of a brute-force search for combinatorial problems and for fixed-length strings.
Learn about the concept of demilitarized zones for cybersecurity and networking.
Study the definition of cross-entropy.
Explore the advantages of ANNs against SVMs, and vice versa.
Explore the principles behind the layout of graphs in drawings.
Learn how to draw basic charts in LaTeX.
Learn how to draw graphs using LaTeX.
Learn about the tools that we can use to generate dependency graphs.
Explore the definition of density in a graph in relation to its size, order, and the maximum number of edges.
Explore methods for identifying the correct size and number of hidden layers in a neural network.
Learn the characteristics of words and bytes and discussed their different relationships with memory and processors.
Learn the basics of the methodology for sentiment analysis and explore public datasets for supervised sentiment analysis.
Explore how to loop over the elements of a matrix in a square spiral pattern.
Explore the concept of correlation for bivariate distributions.
Learn about the difference between classification and clustering.
Explore the ontological and epistemological foundations of randomness.
Explore the concept of policy for reinforcement learning agents
Learn about gradient descent and gradient ascent and when to use them.
Explore the main similarities and differences between support vector machines and neural networks.
Explore the theoretical foundation of support vector machines.
Explore some common cases in which we shouldn’t use RegExes.
Study the syntactic rules for regular expressions.
Study the basic laws of Boolean algebra and learn how to apply them for the simplification of Boolean expressions.
Study the main characteristics of convolutional neural networks.
Discover the reasoning according to which we prefer to use logarithmic functions such as log-likelihood as cost functions for logistic regression.
Explore the main similarities and differences between linear and logistic regression.
Learn the conceptual bases of first-order logic and explore how to derive it as a generalization from propositional logic.
Learn the formal definition of bias in measurements, predictions, and neural networks.
Explore the foundational concepts for propositional logic, which include the idea of proposition and declarative sentences.
Learn the conceptual bases of graph theory.
Learn about the theory behind Knowledge Bases, expert systems, and their associated knowledge graphs.