This post talk about the Power of Offline Reinforcement Learning. RL algorithms that could potentially scale to real-world problems.
Reinforcement learning has grown rapidly in the past few years, from tabular methods that can only solve simple toy problems to powerful algorithms that tackle incredibly complex problems such as playing Go, learning robotic manipulation skills or controlling autonomous vehicles. Unfortunately, adoption of RL for real-world applications has been somewhat slow, and while current RL methods have proven their ability to find high performing policies for challenging problems with high-dimensional raw observations (such as images), actually using them is often difficult or impractical. This is in stark contrast to supervised learning methods, which are highly prevalent in many fields of industry and research and are utilized with great success. Why is that?
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.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
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.
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.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant