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February 21, 2025 · Orange County

Deep Learning Examples

This talk demonstrates how deep learning and neural networks can be applied through code to solve practical business problems in real-world scenarios.

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Tech stack
  • Deep learning
    Deep learning uses multilayered neural networks (DNNs) to automatically learn complex, hierarchical feature representations directly from massive datasets.
    Deep learning (DL) is a subset of machine learning that utilizes artificial neural networks with multiple hidden layers (the 'deep' component) to model high-level data abstractions. This architecture allows the model to perform automatic feature extraction: it learns the optimal features (e.g., edges, then shapes, then objects) instead of requiring manual engineering. DL models power state-of-the-art AI across industries: Convolutional Neural Networks (CNNs) drive image recognition with near-human accuracy, while Transformer models are the foundation for Generative AI like large language models (LLMs) and chatbots (e.g., ChatGPT).
  • Neural networks
    A core subset of machine learning (ANNs), neural networks mimic the human brain's interconnected neurons to process complex data: key applications include image and speech recognition.
    Neural networks operate on a layered architecture (input, hidden, output) of nodes (artificial neurons) with weighted connections. The system learns via backpropagation: processing massive datasets and iteratively adjusting those weights to minimize prediction error. This adaptive, non-linear processing capability drives modern deep learning applications, enabling high-accuracy solutions in critical areas like large language models (GPT, BERT) and autonomous vehicle systems.

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