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4.3. AI in Industry

Artificial Intelligence (AI) is transforming numerous industries by enhancing efficiency, accuracy, and decision-making. This section explores how AI is being applied in three critical domains – Healthcare , Finance , and Autonomous Vehicles – highlighting current trends, technologies, and real-world case studies. We present an organized overview in a logical progression, reflecting both the recent advances and the chronological development of these applications. Healthcare AI has become an indispensable tool in healthcare, improving diagnostics and enabling personalized treatments. AI systems can analyze vast amounts of medical data (from imaging scans to genetic information) much faster and often more accurately than human clinicians. This capability addresses long-standing challenges such as diagnostic errors (estimated at ~5% in outpatient settings) by providing data-driven insights and predictions. AI Diagnostics: In medical diagnostics, AI algorithms – especially deep learni...
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4.2. Generative AI

Generative Adversarial Networks (GANs) Core Concepts Generative Adversarial Networks (GANs) are a class of machine learning frameworks introduced by Ian Goodfellow and colleagues in 2014. A GAN consists of two competing neural networks: a Generator and a Discriminator . The generator’s role is to create synthetic data (e.g. images) resembling the training data, while the discriminator’s job is to distinguish the generator’s fakes from real data. Formally, the two networks are locked in a zero-sum game: the generator tries to “fool” the discriminator by producing outputs that appear real, and the discriminator tries to correctly classify inputs as real or fake. Through this adversarial training process , both models improve – the generator learns to produce more realistic outputs, and the discriminator becomes a better judge. This iterative back-and-forth continues until the discriminator can no longer reliably tell generated data apart from real examples. To train a GAN, one typi...
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4.1. Deep Learning Frameworks

4: Current Trends and Applications in AI 4.1. Deep Learning Frameworks Introduction Deep learning frameworks are specialized software libraries that simplify the development and deployment of neural networks by providing ready-made building blocks (tensor operations, neural network layers, optimizers, etc.) and tools for automatic differentiation. Over the past decade, numerous frameworks have emerged to help researchers and engineers assemble, train, and scale deep learning models without implementing everything from scratch. Early pioneers like Theano (developed at Université de Montréal in 2007) provided a foundation for Python-based deep learning, introducing the concept of constructing mathematical expressions (and their gradients) as computation graphs. Since then, the field has seen a rapid evolution of frameworks – from static graph approaches to more dynamic, Pythonic ones – each balancing ease of use, flexibility, performance, and production readiness. In this post, we ...
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