Vignya Durvasula

Reinforcement Learning is a branch of machine learning that focuses on decision-making to maximize positive outcomes while successfully reaching a goal state. It involves performing certain actions through a trial-and-error method. Reinforcement Learning is used in many real-world applications like Gaming, Recommendation systems, autonomous vehicles, and many more. The agent is expected to explore the

Reinforcement Learning is a fascinating machine learning technique, which differs from the other techniques of ML. In reinforcement learning, the actor(Agent) has to continuously interact with the environment to make decisions, which results in some sort of feedback. In a way, the agent is self-taught and learns about the environment by exploring. But to explore

Reinforcement Learning is a type of machine learning technique where an agent learns to make decisions based on its surroundings by performing some actions in the environment. For every action it performs, it receives a reward or a penalty. The agent’s goal is to maximize positive rewards by making optimal decisions. Q Learning is a

Machine Learning is a growing technology supporting many applications in almost every field. Covering subsets like Deep Learning, machine learning is the study and development of computer machines that learn and adapt to provided data to generalize new data. ML is all about the system learning from the data. Three types of machine learning techniques

Imagine you want to conduct in-depth research about the state-of-the-art machine learning models, and their applications, and implement one of the prominent applications yourself from scratch. You know which machine learning model you would use, and what you want to do after successfully building and training the model. But what is the first and the

There is no doubt that deep learning is taking over the world with its advanced applications like next-word prediction and autonomous vehicles. Deep Learning is used almost everywhere in this era. Fortunately, deep learning also has many networks we can implement for each use case. Recurrent Neural Networks are quoted as a special type of

Mathematics, a field rich in diversity and applications, uses various notations for different measurements in both mathematics and physics. We cannot use the same notation for every measurement we conduct in mathematics and physics, right? To solve this problem, the Greek Mathematicians have introduced the Greek Math symbols for each measurement or variable representing the

Information theory is the study of general collection, storage, and transmission of information. It leverages many concepts such as entropy, to efficiently communicate the information. Kullback-Leibler Divergence (KL Divergence) is one such metric used in information theory to quantify or measure the difference between two distributions. KL Divergence also known as relative entropy is closely

Data is considered a collection of facts and statistics put together for research and analysis. It is always said that data can take many forms. I’m sure we all have our experiences with structured, unstructured, and even big data. Did you come across time series data? Unlike the traditional forms of data such as structured and unstructured