Learn how to use the proportion of variance to choose the number of components in PCA.
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Baeldung Author
Saulo Barreto
Currently, I'm pursuing my Master's Degree at the University of Debrecen. In my field of research in Machine Learning, I'm interested in Generalization Theory, Hyperparameters Analysis, and the influence of synthetic data in training neural networks for Computer Vision applications. Moreover, I'm always trying to expand my knowledge in AI and Computer Science topics.
Here's what I've written (so far):
Baeldung on Computer Science
- All
- Machine Learning (12)
- Deep Learning (5)
- Math and Logic (3)
- Computer Vision (3)
- Programming (2)
- Data Science (2)
- Methodology (1)
What Is a Digital Twin?
Filed under Methodology
Learn about digital twins, models that allow simulation for real-world processes, objects, and places in real time.
What Does Learning Rate Warm-up Mean?
Filed under Machine Learning
Explore different strategies of defining the learning rate in order to warm up the training stage.
How to Calculate Signal-To-Noise Ratio?
Filed under Math and Logic
Learn the definition of SNR and review examples of how to calculate and interpret the SNR in different scenarios.
What Are i.i.d. Random Variables?
Filed under Math and Logic
Learn what it means when we say that random variables are independent and identically distributed and why this isn’t always easy to check.
Open Source Explained
Filed under Programming
Learn about the open-source movement.
What Is Singular Value Decomposition?
Filed under Machine Learning, Math and Logic
Explore the relevance of and how to compute an SVD of a matrix.
Statistics: Correlation vs. Regression
Filed under Data Science
Learn the difference between correlation and regression, two statistical techniques we use to analyze the relationship between variables.
What Is the No Free Lunch Theorem?
Filed under Machine Learning
Learn about the No Free Lunch Theorem.
Differences Between Computer Vision and Image Processing
Filed under Computer Vision
Explore the differences between computer vision and image processing.
What Are Image Histograms?
Filed under Computer Vision
Learn what image histograms are and when to use them.
Instance Segmentation vs. Semantic Segmentation
Filed under Computer Vision, Deep Learning
Explore semantic and instance segmentation.
Data Augmentation
Filed under Deep Learning, Machine Learning
Explore data augmentation techniques.
What Is Fine-Tuning in Neural Networks?
Filed under Machine Learning
Learn about fine-tuning neural networks.
Differences Between Backpropagation and Feedforward Networks
Filed under Deep Learning, Machine Learning
Learn the differences between backpropagation and feedforward neural networks.
Real-Life Examples of Supervised Learning and Unsupervised Learning
Filed under Deep Learning, Machine Learning
Explore some real-life examples of supervised and unsupervised learning.
Markov Decision Process: How Does Value Iteration Work?
Filed under Machine Learning
Learn how to implement a dynamic programming algorithm to find the optimal policy of an RL problem, namely the value iteration strategy.
Choosing a Learning Rate
Filed under Machine Learning
Explore different strategies to update the weights during the training phase of any machine learning model.
How Do “20 Questions” AI Algorithms Work?
Filed under Machine Learning
Learn how we can implement the 20 Questions Game using a nonparametric model called a decision tree.
High-Level Languages vs. Low-Level Languages
Filed under Programming
Explore the differences between high-level and low-level languages.
Why Mini-Batch Size Is Better Than One Single “Batch” With All Training Data
Filed under Deep Learning, Machine Learning
Learn the main differences between using the whole dataset as a batch to update the model and using a mini-batch