Deep Learning

Large Action Models (LAMs) are revolutionizing AI by understanding language, reasoning, and taking action. This guide explores LAMs, their capabilities, and how they’ll transform various industries.

This comprehensive guide walks you through the entire data science process, including data acquisition, preparation, exploration, and modeling.

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.