Search results
Results from the WOW.Com Content Network
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. The predecessor to PPO, Trust Region Policy Optimization (TRPO), was published in 2015.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Specifically designed for Continuous/Lifelong Learning and Object Recognition, is a collection of more than 500 videos (30fps) of 50 domestic objects belonging to 10 different categories. Classes labelled, training set splits created based on a 3-way, multi-runs benchmark.
Offline learning is a machine learning training approach in which a model is trained on a fixed dataset that is not updated during the learning process. [1] This dataset is collected beforehand, and the learning typically occurs in a batch mode (i.e., the model is updated using batches of data, rather than a single input-output pair at a time).
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]
The output weight can be calculated for linear regression with all algorithms whether they are online or offline. In addition to the solutions for errors with smallest squares, margin maximization criteria, so-called training support vector machines, are used to determine the output values. [ 12 ]
MXNet: an open-source deep learning framework used to train and deploy deep neural networks. PyTorch : Tensors and Dynamic neural networks in Python with GPU acceleration. TensorFlow : Apache 2.0-licensed Theano-like library with support for CPU, GPU and Google's proprietary TPU , [ 116 ] mobile