Deep Learning

On 16/12/2024, in conjunction with PGRI's 79th Anniversary and National Teachers' Day (HGN) 2024, the Minister of Primary and Secondary Education (Mendikdasmen), Abdul Mu'ti, said that the deep learning approach can be applied to the current national curriculum, namely Curriculum 2013 (K-13) and Merdeka Curriculum, namely; We have now begun to conduct studies and have completed the studies related to the application of the deep learning approach. Of course, after we publish the ministerial regulation, we will conduct training for teachers related to the application of deep learning.
Deep learning is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the human brain, specifically neural networks. The development of deep learning has gone through several important phases that shaped the technology into what we know today.
Beginnings and Basic Concepts
The basic concept of neural networks dates back to the 1950s, but significant advancements only occurred in 1986 when Geoffrey Hinton and his colleagues introduced the backpropagation algorithm. This algorithm allowed for more efficient training of deep neural networks, which is the foundation of modern deep learning.
Introduction of the Term 'Deep Learning'
In 2006, Geoffrey Hinton officially introduced the term 'deep learning' to describe new algorithms that allow computers to 'see' and distinguish objects in images and videos. This marked an important turning point in the development of this technology, as deep learning began to gain widespread attention from the scientific and industrial communities.
Technology Advancements and Applications
Since then, deep learning has developed rapidly, especially with advances in computing and the availability of big data. Deep learning allows computers to learn autonomously from given data, without the need for explicit programming.
This has paved the way for a wide range of applications, from speech recognition and facial recognition to autonomous vehicles.
Challenges and the Future
Although deep learning has made significant progress, challenges remain, such as the need for large data and intensive computation. However, as technology and research continue to evolve, the future of deep learning looks bright, with the potential to transform many aspects of daily life and industry.
As such, the history of the development of deep learning reflects the long journey from the initial concept of neural networks to a revolutionary and highly influential technology in artificial intelligence today.
Furthermore, Deep learning is an approach that integrates three important aspects: mindful learning, meaningful learning, and joyful learning. This approach emphasizes a deep learning process where students not only receive information, but also develop a full awareness of what they are learning, connect it to their existing knowledge and real-life experiences, and experience the learning process in a fun and pressure-free atmosphere. Through deep learning, students are encouraged to develop their critical, analytical, and creative thinking skills, while still paying attention to their emotional aspects and mental well-being. This approach aims to create a holistic learning experience, where students understand the material deeply, enjoy the learning process, and apply their knowledge in everyday life.
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