Software Engineering is the systematic study of computation, algorithms, and data processing. It focuses on understanding how information is represented, stored, manipulated, and communicated through computer systems. The field spans a broad range of topics, from fundamental theories of computation and programming languages to advanced areas such as artificial intelligence, cybersecurity, and human-computer interaction. Software Engineering is widely applied in fields like software development, data analysis, system design, education, and emerging technologies like robotics and virtual reality.
Experiments: Controlled settings to test hypotheses and establish cause-and-effect relationships. Gathering self-reported data from large groups of people. Observing and recording behavior in natural settings.
Case Studies: sychology helps in understanding not only individual behavior but also social structures, health outcomes, and societal trends.
Loss Function: This function measures the difference between the network’s output and the actual target. Common loss functions include Mean Squared Error (for regression tasks) and Cross-Entropy Loss (for classification tasks).
A framework where two neural networks, a generator and a discriminator, are trained simultaneously. The generator tries to create data that looks real, while the discriminator tries to distinguish between real and fake data. When a model performs well on training data but poorly on unseen data. Techniques like regularization, dropout, and cross-validation are used to mitigate this.
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