Learn about the Information Bottleneck Principle (IB).
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Baeldung Author
Francisco Yepes Barrera
Master’s Degree in Chemistry. My professional activity was carried out in the field of software development and scientific computation.. My interests focus on some areas of artificial intelligence, mainly neural networks and genetic algorithms. In the past I worked on hybrid neuroevolutionary systems and their application to industrial problems. I am currently working on a neural network optimization model called “Eigen Artificial Neural Networks” which, starting from an analogy with physical quantum-mechanical systems, uses wave mechanics techniques in their study.
Here's what I've written (so far):
Baeldung on Computer Science
- All
- Math and Logic (3)
- Machine Learning (3)
- Trees (2)
- Searching (2)
- Networking (2)
- Deep Learning (2)
- Algorithms (2)
- Security (1)
- Data Structures (1)
Hill Climbing Algorithm
Filed under Algorithms
Learn the characteristics of one of the simplest and best-known optimization algorithms: hill climbing.
Applications of Red-Black Trees
Filed under Data Structures, Searching, Trees
Learn about important applications of Red-Black trees.
Calculating the Parity Bit of a Bit Sequence
Filed under Networking
Learn a simple technique for checking errors in the transmission of a binary string
Calculate Upload/Download Speed Using Ping
Filed under Networking
Learn how we can use ping to measure the bandwidth of a network connection.
Sine Cosine Algorithm
Filed under Algorithms, Math and Logic
Learn about the sine-cosine algorithm.
Primality Test: Miller-Rabin Method
Filed under Math and Logic
In this tutorial, we’ll study one of the methods used to verify if n is prime without resorting to factorization: the Miller-Rabin method.
Understanding Complexity of Cryptographic Algorithms
Filed under Security
Examine computational complexity issues within cryptographic algorithms
Correlated Features and Classification Accuracy
Filed under Machine Learning, Math and Logic
Learn how correlation phenomena are generally harmful within a predictive method.
Random Initialization of Weights in a Neural Network
Filed under Deep Learning, Machine Learning
Study weight initialization techniques in artificial neural networks and why they’re important.
Time Complexity of Searching in a Balanced Binary Search Tree
Explore a binary search tree data structure time complexity.