- THE DEEP LEARNING PROCESS AND THE CONSTRUCTION OF KNOWLEDGE

2014 ◽  
pp. 40-73
Keyword(s):  
Deep Learning ◽  
Learning Process ◽  
Construction Of Knowledge

scholarly journals Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 39
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Objective Function ◽  
Learning Process ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Core ◽  
Learning Tasks ◽  
Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

Constructivism Theory in Technology-Based Learning

2019 ◽  
pp. 268-287
Author(s):  
Lazarus Ndiku Makewa
Keyword(s):  
Learning Process ◽  
Dynamic Process ◽  
Learning Theories ◽  
Active Engagement ◽  
Learning Tool ◽  
Effective Learning ◽  
Technology Knowledge ◽  
Construction Of Knowledge ◽  
Use Of Technology ◽  
Tool Technology

Learning theories are conceptual frameworks describing how knowledge is absorbed, processed, and retained during learning. This chapter will explore constructivism theory and how it determines impact on technology knowledge in instruction. Constructivism theory states that learning occurs when one constructs both mechanisms for learning and his or her own unique version of the knowledge. It states that knowledge must be constructed by the learner. The teacher can only assist the learner to do the construction. The construction of knowledge is a dynamic process that requires the active engagement of the learners who will be responsible for one's learning while the teacher only creates an effective learning environment. As students and teachers make use of technology in the learning process, these skills become necessary and the technology becomes a learning tool. Technology can serve as coaches by locating the problem and allowing for as much rehearsal, practice, and help as necessary to accomplish the task. Technology can enhance the cognitive powers of students.

The Methodological Context in Higher Education

2021 ◽  
pp. 15-36
Author(s):  
María-Mercedes Rojas-de-Gracia ◽  
Pilar Alarcón-Urbistondo
Keyword(s):  
Higher Education ◽  
21St Century ◽  
Learning Process ◽  
Learning Situation ◽  
Rigorous Approach ◽  
Construction Of Knowledge ◽  
Teaching Learning ◽  
Collective Construction ◽  
The Way

Given the limited number of documents addressing methodological context in higher education with a rigorous approach, this chapter comprises a document drawn up in order to clarify methodological concepts. It emphasizes the importance of the teaching-learning process and the significance of placing the student at the center of all actions. The educator's mission changes from being a mere transmitter of information to being a conductor and organizer of the learning situation. To achieve this, several methods must be combined, requiring a balance between the theoretical and practical classes. Likewise, they can be benefited by carrying out complementary activities. This combination is intended to face the great challenges of higher education in the 21st century, which are driven by changes in the way students learn. The emergence of technologies means that the protagonist in the collective construction of knowledge is the student, responding to their digital and participatory demands.

scholarly journals Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model

2020 ◽  
Vol 10 (21) ◽  
pp. 7751
Author(s):  
Seong-Jae Hong ◽  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Deep Learning ◽  
Learning Process ◽  
Intensity Difference ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Model ◽  
Learning Techniques ◽  
Vessel Detection ◽  
Intensity Images ◽  
Deep Learning Model

Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.

Mixed Approaches to Learning in the Flipped Classroom: How Students Approach the Learning Environment

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Margrét Sigrún Sigurðardóttir ◽  
Thamar Melanie Heijstra
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Learning Environment ◽  
Learning Process ◽  
Flipped Classroom ◽  
Approaches To Learning ◽  
Male Students ◽  
Learning Approach ◽  
Learning Approaches ◽  
Surface Approach

Flipped teaching is a trend within higher education. Through flipped teaching the learning environment can be altered by moving the lecture out of the classroom through online recordings, while in-classroom sessions focus on active learning and engaging students in their own learning process. In this paper, we used focus groups comprised of male students in a qualitative research course with the aim of understanding the ways in which we might improve active student engagement and motivation within the flipped classroom. The findings indicated that, within the flipped classroom, students mix surface and deep-learning approaches. The online recordings, which students interact with through a surface approach, can function as a stepping stone toward a deep-learning approach to in-class activities, but only if students come to class prepared. The findings therefore suggest that students must be made aware of the importance of preparation prior to flipped classroom in-class activities to ensure the active learning process is successful. By not listening to the recordings (e.g., due to technological failure, as was the case in this study), students can result in only employing a surface approach.

scholarly journals Predicting Groundnut Disease using CNN Models

2021 ◽  
Vol 23 (06) ◽  
pp. 756-766
Author(s):  
Neha Suresh ◽  
◽  
Dr.AnandiGiridharan Dr.AnandiGiridharan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Arachis Hypogaea ◽  
Efficient Method ◽  
Learning Process ◽  
Plant Disease ◽  
Disease Classification ◽  
Agricultural Field ◽  
Arachis Hypogaea L ◽  
Overall Performance

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.

scholarly journals Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis

2021 ◽  
Vol 62 (12) ◽  
pp. 1125
Author(s):  
Jang-Hoon Oh ◽  
Hyug-Gi Kim ◽  
Kyung Mi Lee ◽  
Chang-Woo Ryu ◽  
Soonchan Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Learning Process ◽  
Maxillary Sinusitis ◽  
Independent Task ◽  
Task Learning ◽  
End To End
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