`machinelearning` Related Article Results

Dropout is a regularization method developed to prevent overfitting in deep learning models and is now widely used. It was first proposed by Geoffrey Hinton and colleagues in 2012 and later thoroughly analyzed in 2014 under the leadership of Nitish Srivastava. Dropout çalışma prensibi tasviri (Yapay Zekâ ile oluşturulmuştur) " image-element-format="right" image-source="https://cdn.t3pedia.org/media/uploads/2025/06/22/OSfR2gHLwJOEcOthhc3dmhlyiiW1vYk7.png"> Mechanism of Operation Dropout is a r

In classical algorithmic governance, the subject who sets the rules, accepts their logic, and modifies them when necessary is the human. The machine’s regularity is a tool, but the true safeguard is human reasoning. When artificial intelligence is involved, however, the dominant idea is that a mind free from human shortcomings may exist. Even if we cannot fully comprehend the wisdom behind its decisions, the possibility that one day we might entrust our own will to this intelligence is dangerous

In recent years, artificial intelligence and machine learning have become among the most discussed topics in healthcare. Artificial intelligence applications have entered our lives across numerous fields, from image analysis systems used in cancer screening to algorithms that predict risks in intensive care unitsartificial intelligence applications. Some studies have shown that artificial intelligence can achieve accuracy rates close to those of expert physicians in diagnosing certain diseasesAn

Artificial intelligence has come to the forefront through its ability to perform human-like cognitive tasks such as learning, decision-making, and problem-solving. Today, it has moved beyond being merely a research field and is being applied across numerous domains including healthcare, education systems, transportation, and manufacturing. This rapid expansion carries the potential to profoundly transform our ways of life, work, and social interactions. Impacts on the Economy and Labor Force Art

Advanced Rocket Motor Technologies: A multidisciplinary field of research and engineering that integrates chemical and electric propulsion concepts, advanced material solutions, novel propellants, and data-driven design tools. This field aims to optimize propulsion systems in terms of thrust, reusability, cost, and environmental impact. Current research focuses on enhancing classical liquid and solid motor architectures while developing new technologies such as hybrid motors, green propellants,

Kuantizasyon, büyük dil modellerinin (LLM – Large Language Models) parametrelerinin daha düşük bit genişlikleriyle temsil edilerek bu modellerin daha az bellek kullanması ve daha verimli çalışması amacıyla uygulanan bir optimizasyon tekniğidir. Büyük dil modelleri, milyarlarca parametre içeren derin sinir ağlarıdır. Metin üretimi, özetleme ve çeviri gibi doğal dil işleme görevlerinde yüksek başarı gösteren bu modellerin çalıştırılması, önemli ölçüde hesaplama gücü, bellek ve enerji gerektirir. Ö

NumPy (Numerical Python), yüksek performanslı bilimsel hesaplamalar için kullanılan açık kaynaklı bir Python kütüphanesidir. Sayısal verilerle etkin çalışmayı sağlar ve çok boyutlu diziler (arrays), matrisler ve bu diziler üzerinde yüksek seviyeli matematiksel işlemler gerçekleştirmek için kapsamlı araçlar sunar. Python programlama dilinde veri analizi, makine öğrenimi, görüntü işleme, sinyal işleme gibi birçok alanda temel taşlardan biri olarak kabul edilir. Tarihçe NumPy'nin kökeni, 1995 yılın

Today, data and digital have become among the world’s most important assets. Data science is a discipline developed to analyze these data transform them into meaningful insights and contribute to decision-making processes. Machine learning, encompassing statistics big data processing and data visualization such as data science plays a critical role across various industries. Areas Covered by Data Science (Credit: maarificedid.com) " image-alternate="" image-element-format="justify"> Data Scie

Supervised learning is one of the fundamental types of machine learning and relies on learning from labeled examples in a data dataset. This method enables an algorithm to be trained to map a given input to the correct output (label). Supervised learning is commonly used in classification and regression like tasks. Key Features Data Labeling: Supervised learning algorithms are trained with knowledge of the correct output value for each training example. That is, the correct result (label) corres

Today, data has become one of the most important assets in the digital world. Data science is a discipline developed to analyze these data, transform them into meaningful insights, and support decision-making processes. Machine learning, along with statistics, big data processing, and data visualization among other fields, plays a critical role in various industries. Fields Covered by Data Science (Credit: maarificedid.com) " image-alternate="" image-element-format="justify"> Data Science Met