Deep Learning

Capsule Networks tries to solve the limitations of CNNs by preserving information, using a routing algorithm.

Attention mechanisms enable models to focus on specific parts of input data, enhancing their ability to process sequences effectively.

Batch normalization, CNN, neural network, network layers, deep learning method, gradient descent, convolutional neural networks

Representation learning is a subfield of machine learning focused on transforming data into compressed representations.

Grounded-SAM offers the best of both worlds in image segmentation, combining Grounding DINO with Segmentation Anything.

TikTok's Depth Anything model is a groundbreaking depth estimation framework. The newly published paper lays out everything you need to know.

EfficientNet is a CNN architecture that utilizes a compound scaling method to uniformly scale depth, width, and resolution.

OpenAI Sora is a text-to-video model that creates realistic and imaginative scenes from textual prompts with a diffusion transformer model.

Midjourney vs Stable Diffusion are two of the leading AI art generators from the AI boom. We explore their strengths and weaknesses.

Graph Neural Networks (GNNs) operate on graph-structured data, enabling them to learn relationships and patterns within complex networks.

DETR is a method for object detection with transformers. Explore its architecture, how it predicts bounding boxes and labels, and use cases.

Is AGI already here? We discuss everything you need to know about the 3 types of artificial intelligence in a complete guide.