We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Concepts and algorithms of machine learning including version-spaces, decision trees, instance-based learning, networks, evolutionary computation, Bayesian learning and reinforcement learning.

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

Artificial Intelligence (AI) has become a buzzword in today’s tech-driven world, promising new possibilities and reshaping industries. Despite its prevalence, ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of ...

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a specific approach to reach the same goal.

Through a novel combination of machine learning and atomic force microscopy, researchers in China have unveiled the molecular ...

Intelligent organizations prioritize investments in machine learning and real-time data to improve decision making, accelerate revenue generation efforts, reduce operational expenses and protect ...

This paper comprehensively surveys existing works of chip design with ML algorithms from an algorithm perspective. To accomplish this goal, the authors propose a novel and systematical taxonomy for ...