Deep Learning 50+ Reinforcement Learning Key Terms: Understanding the Language of RL In this article, we have covered 50+ Key Terms in the domain of Reinforcement Learning. This will give a strong hold on RL.
Deep Learning Policy Gradient in RL Policy gradient is a popular approach in RL that is used to learn a policy function that maps states to actions, by directly optimizing the expected return of the policy.
Deep Learning Resilient Backpropagation (Rprop): The Robust Optimization Algorithm for Training Deep Neural Networks Resilient Backpropagation (Rprop) is a popular optimization algorithm used in training artificial neural networks. The algorithm was first introduced by Martin Riedmiller and Heinrich Braun in 1993.
Deep Learning Layer Normalization: An Essential Technique for Deep Learning Beginners Layer normalization is a relatively new technique in the field of deep learning. It was first introduced by Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey Hinton in their 2016 paper "Layer Normalization".
Deep Learning Multi-Layer Perceptron (MLP): A Basic Understanding Multi Layer Perceptron (MLP) is a type of artificial neural network that is widely used for various machine learning tasks such as classification and regression. It is called a multi-layered perceptron because it has many layers of nodes (known as artificial neurons) that connect to each other.
