Learn about Recurrent Neural Networks (RNNs).
Baeldung Author
Panagiotis Antoniadis
Here's what I've written (so far):
Baeldung on Computer Science
- All
- Machine Learning (15)
- Deep Learning (15)
- Computer Vision (9)
- Math and Logic (5)
- Algorithms (4)
- Artificial Intelligence (3)
What is Cosine Similarity?
Filed under Math and Logic
Explore cosine similarity and its applications.
Optimization: Gradient-Based Algorithms
Filed under Algorithms, Math and Logic
Learn about gradient-based algorithms in optimization.
Introduction to Inception Networks
Filed under Artificial Intelligence
Explore the concept of Inception Networks.
Residual Networks
Filed under Computer Vision
Learn about the Residual Networks.
Optimization: Local vs. Global Optima
Filed under Math and Logic
Learn about the global and local optima.
Optimization: Objective Functions, Decision Variables and Constraints
Filed under Math and Logic
Learn about mathematical optimization.
How Do Blurs In Images Work?
Filed under Computer Vision
Learn about how the blur operation works in images.
Spatial Pyramid Pooling
Filed under Computer Vision
Explore the Spatial Pyramid Pooling (SPP) layer.
Computer Vision: Popular Datasets
Filed under Computer Vision
Explore three popular datasets in computer vision.
How to Handle Large Images to Train CNNs?
Filed under Computer Vision
Explore three ways of using large images as input to CNNs.
Neural Networks: What Is Weight Decay Loss?
Filed under Deep Learning, Machine Learning
Learn about the weight decay loss.
Neural Networks: Difference Between Conv and FC Layers
Filed under Deep Learning, Machine Learning
Explore the Conv and the FC layer of a neural network.
Scale-Invariant Feature Transform
Filed under Machine Learning
Learn about the Scale-Invariant Feature Transform (SIFT).
Machine Learning: What Is Ablation Study?
Filed under Machine Learning
Learn about the term ablation study in the field of machine learning
Differences Between Epoch, Batch, and Mini-batch
Filed under Artificial Intelligence
Explore the differences between an epoch, a batch, and a mini-batch.
Cross-Validation: K-Fold vs. Leave-One-Out
Filed under Deep Learning, Machine Learning
Explore the differences between k-fold leave-one-out cross-validation techniques.
How to Compute the Similarity of Colours
Filed under Algorithms
Explore three common methods for computing the similarity between colors.
How to Convert a Color From HSL to RGB
Filed under Algorithms
Learn about a method that converts a color from HSL to RGB.
Mean Average Precision in Object Detection
Filed under Computer Vision, Deep Learning
Learn about the mAP metric for object detection.
Hidden Layers in a Neural Network
Filed under Deep Learning, Machine Learning
Learn about the hidden layers in a neural network.
An Introduction to Self-Supervised Learning
Filed under Machine Learning
Learn about self-supervised learning.
Latent Space in Deep Learning
Filed under Deep Learning, Machine Learning
Learn about the latent space in deep learning.
Activation Functions: Sigmoid vs Tanh
Filed under Deep Learning, Machine Learning
Explore two activation functions, the tanh and the sigmoid.
An Introduction to Contrastive Learning
Filed under Deep Learning, Machine Learning
Learn about contrastive learning.
Algorithms for Image Comparison
Filed under Deep Learning, Machine Learning
Explore three algorithms for image comparison
Image Processing: Occlusions
Filed under Deep Learning, Machine Learning
Learn about occlusions in image processing.
How to Convert an RGB Image to a Grayscale
Filed under Algorithms
Learn how to convert an RGB image to grayscale.
Applications of Generative Models
Filed under Computer Vision, Deep Learning
Learn about various applications of generative models.
Calculate the Output Size of a Convolutional Layer
Filed under Computer Vision, Deep Learning
Learn about computing the outputs size of a convolutional layer.
Introduction to Curve Fitting
Filed under Artificial Intelligence, Math and Logic
Learn about curve fitting and the least-squares algorithm.
An Introduction to Generative Adversarial Networks
Filed under Deep Learning, Machine Learning
Learn about Generative Adversarial Networks (GANs).
Using GANs for Data Augmentation
Filed under Computer Vision, Deep Learning
Explore how we can use GANs for data augmentation.
Features, Parameters and Classes in Machine Learning
Filed under Machine Learning
Learn about three key components of a Machine Learning (ML) model: Features, Parameters, and Classes.
Decision Tree vs. Naive Bayes Classifier
Filed under Machine Learning
Take a look at two of the most well-known classifiers, Naive Bayes and Decision Trees.