EfficientNet: Compound Scaling of Neural Networks
EfficientNet uses compound scaling to balance depth, width, and resolution. Complete guide with PyTorch code.
EfficientNet: Compound Scaling of Neural Networks LireLearn Python
Explore in-depth articles on programming, analyses of innovative GitHub projects, and guides on advanced Python frameworks and techniques.
SOM: Kohonen Self-Organizing Maps
SOMs project multidimensional data onto a 2D grid while preserving topology. Complete guide with Python code.
SOM: Kohonen Self-Organizing Maps Lire
YOLO: Real-Time Object Detection
YOLO detects and classifies objects in a single neural network pass. Complete guide covering YOLOv1 to YOLOv8 with Python code.
YOLO: Real-Time Object Detection Lire
EWC: Elastic Weight Consolidation for Continual Learning
EWC protects the important weights of a previous task during the learning of a new task, reducing catastrophic forgetting. Complete guide with PyTorch.
EWC: Elastic Weight Consolidation for Continual Learning Lire
CatBoost: Gradient Boosting for Categorical Data
CatBoost natively handles categorical features without prior encoding thanks to ordered target encoding. Complete guide with Python code.
CatBoost: Gradient Boosting for Categorical Data Lire
LightGBM: Optimized and Fast Gradient Boosting
LightGBM is an ultra-fast gradient boosting framework that uses leaf-wise growth and histogram-based sampling. Complete guide with Python code.
LightGBM: Optimized and Fast Gradient Boosting Lire
U-Net: Image Segmentation with U-Shaped Networks
U-Net segments images pixel by pixel with an encoder-decoder architecture and skip connections. Complete guide with PyTorch code.
U-Net: Image Segmentation with U-Shaped Networks Lire
DenseNet: Densely Connected Networks
DenseNet connects every layer to all subsequent layers for maximum feature reuse. Complete guide with PyTorch code.
DenseNet: Densely Connected Networks Lire
ResNet: Residual Networks and Skip Connections
ResNet revolutionizes deep learning with residual blocks enabling training of networks with hundreds of layers. Complete guide with PyTorch code.
ResNet: Residual Networks and Skip Connections Lire
Dropout: Regularization by Random Deactivation
Dropout regularizes neural networks by randomly deactivating neurons during training. Complete guide with variants and PyTorch code.
Dropout: Regularization by Random Deactivation Lire