Deep Learning

Explore the concept of liquid neural networks, how they compare to traditional neural networks, and their applications.

The Xception model is a Computer vision model that utilizes depthwise separable convolution and is a successor of Inception architecture.

Text annotation labels and tags textual data for NLP model training in sentiment analysis, entity recognition, language translation, and more

Neural Radiance Fields (NeRFs) are a deep learning technique that is revolutionizing the way we represent and interact with 3D scenes. Discover the core concepts behind NeRFs novel view synthesis, learn about cutting-edge variations, explore their applications and a code example.

GoogLeNet is an Image Classification model that is made by stacking Inception Modules. Released in 2014, it surpassed previous benchmarks.

Spatio-Temporal Action Recognition encompasses the complex task of understanding actions in videos, including what action is happening and when and where it occurs.

MobileNet, introduced in 2017 by a team of researchers at Google, is a Deep Learning model for Smartphones, IoT, and embedded devices.

Explore the mechanics of Spatial Transformer Networks, their integration with Convolutional Neural Networks (CNNs), and real-world applications through insightful case studies.