CodeEmporium Courses

ResNet - Explained

Discover ResNet architecture and skip connections to solve vanishing gradients and performance degradation in deep neural networks with practical code examples.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

YOLO - You Only Look Once Object Detection Algorithm Explained

Discover YOLO V1 object detection network architecture, training process, loss functions, and advantages over R-CNN methods in computer vision applications.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Feature Pyramid Networks - Enhancing Convolutional Network Performance Explained

Discover how Feature Pyramid Networks enhance convolutional network performance for computer vision tasks, with practical code examples and implementation details.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Mask R-CNN Explained

Discover how Mask R-CNN extends Faster R-CNN for instance segmentation, covering RoIAlign, training processes, loss computation, and practical inference implementation.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Depthwise Separable Convolutions - Explained

Discover how depthwise separable convolutions work, their computational advantages over standard convolutions, and implement them with practical code examples.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

R-CNN - Regions with Convolutional Neural Networks Explained

Explore R-CNN architecture for object detection through region proposals, CNN feature extraction, and SVM classification with training and inference processes.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Image Segmentation - Explained

Discover what image segmentation is, why it's essential in computer vision, and implement graph-based segmentation techniques with practical code examples.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Region Proposals in Computer Vision - What, Why, and How with Selective Search

Understand region proposals in computer vision: what they are, why they're essential for object detection, and how to implement selective search with practical code examples.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Deconvolution - What Do Networks Learn? Visualization and Code

Uncover what deep layers of convolutional neural networks actually learn through deconvolution visualization techniques with practical code examples and visual demonstrations.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Inception Network - Explained with Code

Explore the Inception network architecture, understand its unique design principles, and implement it with hands-on coding to see how this funky-looking neural network achieves superior performance.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Pointwise Convolutions - Explained with Code

Discover 1x1 convolutions in neural networks through architecture diagrams and PyTorch implementation, exploring their benefits for dimensionality reduction and computational efficiency.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

VGGNet - Deep Convolutional Neural Network Architecture Explained

Explore VGGNet architecture fundamentals: understand its depth, 3x3 convolution advantages, coding implementation, and performance comparison with AlexNet in this comprehensive tutorial.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

Why Convolutional Networks Work So Well on Images

Discover why convolutional neural networks excel at image processing through architectural advantages like parameter sharing, translation invariance, and spatial hierarchy.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Visualizing Convolutional Neural Networks - Feature Maps and Learning Process

Explore how convolutional neural networks learn through visual analysis of feature maps, comparing different architectures from basic fully connected to complex stacked layers.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Why Neural Networks Are So Deep - AlexNet Explained

Explore the evolution of deep neural networks through AlexNet's groundbreaking architecture, ReLU activation, GPU training, and overfitting solutions.

• YouTube
• 23 minutes
• Self-Paced
• Free Video