Research on Network Learning Process from Perspective of Communication

2013 ◽  
pp. 689-695
Author(s):  
Yuehui Zhou
Keyword(s):  
Learning Process ◽  
Network Learning

scholarly journals Tutorial on Using Machine Learning for Activity Recognition Via a Smartphone

Telecom IT ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 92-108
Author(s):  
I. Zelichenok ◽  
R. Pirmagomedov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Collection ◽  
Activity Recognition ◽  
Learning Process ◽  
Daily Activity ◽  
Application Development ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network

This article provides a tutorial for developing a simple machine learning application in Python. More spe-cifically, the paper considers daily activity recognition using sensors of a smartphone. For development, we used TensorFlow, Skikit learn, NumPy, Pandas, and Matplotlib. The paper explains in detail the main steps of the application development, including data collection and pre-processing, design of the neural network, learning process, and use of a trained model. The overall accuracy of the developed application when recognizing the activity is about 95 %. This paper can be useful for students and specialists who want to start work on machine learning.

scholarly journals Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization

2021 ◽  
Vol 11 (3) ◽  
pp. 1131
Author(s):  
Liwei Hou ◽  
Hengsheng Wang ◽  
Haoran Zou ◽  
Qun Wang
Keyword(s):  
Reinforcement Learning ◽  
Learning Process ◽  
Gradient Methods ◽  
Policy Network ◽  
Practice Phase ◽  
Network Learning ◽  
Network Parameters ◽  
Model Free ◽  
Policy Gradient ◽  
Skills Learning

Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great potential in solving robot skills learning problems. However, policy gradient methods require too many instances of robot online interaction with the environment in order to learn a good policy, which means lower efficiency of the learning process and a higher likelihood of damage to both the robot and the environment. In this paper, we propose a two-phase (imitation phase and practice phase) framework for efficient learning of robot walking skills, in which we pay more attention to the quality of skill learning and sample efficiency at the same time. The training starts with what we call the first stage or the imitation phase of learning, updating the parameters of the policy network in a supervised learning manner. The training set used in the policy network learning is composed of the experienced trajectories output by the iterative linear Gaussian controller. This paper also refers to these trajectories as near-optimal experiences. In the second stage, or the practice phase, the experiences for policy network learning are collected directly from online interactions, and the policy network parameters are updated with model-free reinforcement learning. The experiences from both stages are stored in the weighted replay buffer, and they are arranged in order according to the experience scoring algorithm proposed in this paper. The proposed framework is tested on a biped robot walking task in a MATLAB simulation environment. The results show that the sample efficiency of the proposed framework is much higher than ordinary policy gradient algorithms. The algorithm proposed in this paper achieved the highest cumulative reward, and the robot learned better walking skills autonomously. In addition, the weighted replay buffer method can be made as a general module for other model-free reinforcement learning algorithms. Our framework provides a new way to combine model-based reinforcement learning with model-free reinforcement learning to efficiently update the policy network parameters in the process of robot skills learning.

GAYA BICARA PADA STAF PENGAJAR JURUSAN AKUNTANSI POLITEKNIK NEGERI SEMARANG DI DALAM KEGIATAN PROSES BELAJAR MENGAJAR

2019 ◽  
Vol 2 (2) ◽  
pp. 165
Author(s):  
Pandiya Pandiya Pandiya ◽  
Nurul Hamida Hamida
Keyword(s):  
Data Analysis ◽  
Learning Process ◽  
Teaching And Learning ◽  
Sampling Technique ◽  
Body Language ◽  
Teaching Staff ◽  
Applied Method ◽  
Presentation Method ◽  
Study Support ◽  
Speaking Style

This study aims to determine the extent to which the teaching staff in the Semarang State Polytechnic Accounting Department applies a style of speech; i.e. either oratory, deliberative, consultative, relaxed, or intimate. The data collection is done by questionnaire and class observation. The population of this study consists of teaching staff at the Semarang State Polytechnic Accounting Department. The data is more qualitative, which is more in the form of a description of the characteristics of the respondents and not much related to the numbers. Sampling technique is done by population, namely all teaching staff of Semarang State Polytechnic Accounting Department. Data analysis is carried out by a Likert Scale of 5. The results indicate that Consultative Speech is the style most widely applied by the Teaching Staff of the Semarang State Polytechnic Accounting Department, while the oratoric speaking style is the least practiced style. Extemporan presentation method is the most widely applied method in the activities of the Teaching and Learning Process in the Semarang State Polytechnic Accounting Department, while the impromptu method is the least applied method. The most widely used body language is a smile, while eye flicker is the least applied body language. The distance between the Teaching Staff and Students in the Teaching and Learning Process activities that are most widely applied are groups (125-350 cm), while the least applied distance is intimate (50 cm). The results of this study support the previous research that the use of Body Language greatly affects the success of the Teaching and Learning process.

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