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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.
I am teaching a graduate level deep learning course at LMU. My talk will have some code showing how Deep Learning code can be used to solve real business problems.
Lead Data Scientist portfolio showcasing predictive modeling, NLP, Deep Learning, and AWS/GCP.
- Deep learningDeep 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 networksA 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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