Transfer Learning: Transfer Learning
Transfer Learning reuses pre-trained models for new tasks. Complete guide with fine-tuning and Keras code.
Transfer Learning: Transfer Learning LireLearn Python
Explore in-depth articles on programming, analyses of innovative GitHub projects, and guides on advanced Python frameworks and techniques.
U-Net: Image Segmentation Architecture
U-Net is the reference architecture for semantic image segmentation with skip connections. Complete guide with Keras code.
U-Net: Image Segmentation Architecture Lire
SOM (Self-Organizing Map): Kohonen Self-Organizing Map
The SOM projects high-dimensional data onto a 2D grid while preserving topology. Complete guide with Python code.
SOM (Self-Organizing Map): Kohonen Self-Organizing Map Lire
GAN (Generative Adversarial Network): Generative Adversarial Networks
GANs train a generator and a discriminator in competition to produce realistic data. Complete guide with Keras code.
GAN (Generative Adversarial Network): Generative Adversarial Networks Lire
VAE (Variational Autoencoder): Variational Autoencoder
The VAE learns a latent distribution to generate new data. Complete guide with reparameterization trick and KL divergence.
VAE (Variational Autoencoder): Variational Autoencoder Lire
Autoencoder: Dimensionality Reduction with Neural Networks
The autoencoder learns a compressed representation of data through a bottleneck. Complete guide with Keras Python code.
Autoencoder: Dimensionality Reduction with Neural Networks Lire
t-SNE: High-Dimensional Data Visualization
t-SNE reduces high-dimensional data to 2D/3D for visualization. Complete guide with perplexity and Python code.
t-SNE: High-Dimensional Data Visualization Lire
PCA (Principal Component Analysis): Dimensionality Reduction
PCA reduces data dimensionality by maximizing explained variance. Complete guide with SVD decomposition and Python code.
PCA (Principal Component Analysis): Dimensionality Reduction Lire
Voting Ensemble: Majority Voting of Models
Voting Ensemble combines the predictions of multiple models through majority voting or weighted averaging. Complete guide with Python code.
Voting Ensemble: Majority Voting of Models Lire
Stacking: Model Stacking
Stacking combines multiple base models through a meta-model. Complete guide with StackingClassifier and Python code.
Stacking: Model Stacking Lire