An Anology Between Animals And Computers. Below is a very famous video by Matthias Wandel. He is making different types of mazes and is observing the mice while they were exploring different mazes. The mouse is learning intelligent behavior in complex dynamic environments.
Reinforcement Learning Reinforcement learning(RL) is a type of deep learning that has been receiving a lot of attention in the past few years. It is useful for the situations we want to train AI for certain skills we don’t fully understand. RL has an agent that takes actions in an uncertain environment with the goal of maximizing the cumulative reward. The agent learns from its mistakes and its decision-making algorithm improves. When I read about the RL concepts, I thought it was very similar to how animals learn and make decisions.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
Dummies guide to Reinforcement learning, Q learning, Bellman Equation. You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.