CodeEmporium Courses

Fast R-CNN - Explained

Explore Fast R-CNN architecture, training methods, and inference techniques for improved object detection performance compared to original R-CNN networks.

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

Faster R-CNN Explained

Discover how Faster R-CNN revolutionizes object detection through its Region Proposal Network, making it significantly faster than previous R-CNN architectures while maintaining accuracy.

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

Vision Transformers - Explained

Discover how Vision Transformers revolutionize computer vision by applying transformer architecture to images, covering pretraining, fine-tuning, and practical implementation.

• YouTube
• 22 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

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

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

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

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

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

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

Swin Transformer Explained

Discover Swin Transformers: hierarchical vision architecture using windowed attention, shifted windows, and patch merging for efficient image processing and computer vision tasks.

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

CLIP - Contrastive Language-Image Pretraining Explained

Explore CLIP's contrastive language-image pretraining, zero-shot inference capabilities, training methodology, and practical implementation with hands-on code examples.

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

Computer Vision 101 - Neural Networks

Explore computer vision fundamentals through neural networks, covering eye structure, visual pathways, CNNs, object detection, and advanced architectures like ResNet and YOLO.

• YouTube
• 8 hours 47 minutes
• Self-Paced
• Free Video

The Math You Should Know for Machine Learning

Master essential mathematical concepts for machine learning including probability, linear regression, PCA, gradient descent, and neural network backpropagation.

• YouTube
• 4 hours 53 minutes
• Self-Paced
• Free Video

Deep Learning 101 - Zero to Hero for Deep Learning

Discover neural networks from scratch with hands-on implementation, backpropagation, optimizers, loss functions, and advanced techniques like transfer learning and NLP applications.

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