Deep reinforcement learning is a subfield of machine learning that combines reinforcement learning and deep learning to allow agents to make decisions from unstructured input data. It has been used for a variety of applications, such as robotics, video games, natural language processing, computer vision, and healthcare. Deep RL algorithms are able to take in large inputs and decide what actions to perform to optimize an objective.
Stanford University
Fall 2022
An in-depth course focused on building neural networks and leading successful machine learning projects. It covers Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Students are expected to have basic computer science skills, probability theory knowledge, and linear algebra familiarity.
No concepts data
+ 35 more conceptsUniversity of Washington
Autumn 2019
A survey course on neural network implementation and applications, including image processing, classification, detection, and segmentation. The course also covers semantic understanding, translation, and question-answering applications. It's ideal for those with a background in Machine Learning, Neural Networks, Optimization, and CNNs.
No concepts data
+ 13 more conceptsStanford University
Autumn 2022-2023
Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.
No concepts data
+ 88 more concepts