Ryan Lafler
CEO, Chief Data Scientist, and Lead Consultant at Premier Analytics Consulting, LLC
San Diego, California, United States
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Ryan Paul Lafler is the CEO, Chief Data Scientist, and Lead Consultant at Premier Analytics Consulting, LLC, a consulting firm based in San Diego, California. He’s an Adjunct Professor at San Diego State University for the Big Data Analytics Graduate Program and the Department of Mathematics and Statistics. Ryan’s multilingual experience in Python, R, SAS, JavaScript (React.js & API frameworks), and SQL has contributed to his success as a Big Data Scientist; Machine Learning Engineer; Statistician; and Application Developer. He received his Master of Science in Big Data Analytics from San Diego State University in May 2023 following the successful defense and publication of his thesis. He holds a Bachelor of Science in Statistics and minored in Quantitative Economics from San Diego State University after graduating Magna cum Laude. His passions include Machine Learning, Deep Learning, Artificial Intelligence, Statistics, full-stack application and interactive dashboard development, data visualization, and Open-Source programming languages.
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Topics
Charting Your Machine Learning Roadmap: Avoiding Rush Hour Hype and Achieving Ethical Results
Machine Learning is experiencing a golden age of investment, democratization, and accessibility across all sectors and industries encompassing the life sciences, healthcare, financial technology (fintech), consumer marketing, e-commerce, manufacturing, and more. But what exactly is Machine Learning? How is it connected to Artificial Intelligence (AI)? And most importantly, how can data scientists, programmers, software engineers, researchers, policy-makers, and entire organizations ethically undertake their endeavors in Machine Learning?
This presentation seeks to answer these questions, and more, by covering the subdivisions, approaches, and objectives underlying Machine Learning. Attendees are given a roadmap to help them navigate supervised, semi-supervised, and unsupervised ML approaches in addition to Deep Learning methodologies. Discussions about data bias, training bias, algorithm assumptions, and model generalization are all addressed to investigate the ethical implications of Machine Learning in both industry and research. By removing the “hype” surrounding Machine Learning approaches, this presentation seeks to empower decision-makers and stakeholders with a better understanding of the complexities, limitations, and potential surrounding ML applications in today’s era of rapidly growing Big Data.
Introduction to Deep Learning with Python: Building Neural Networks with Keras & TensorFlow
Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are a subset of Deep Learning that involves layers of neurons interacting with, transforming, and passing data through successive layers to develop highly flexible and robust predictive models.
Whether they’re used for regression, classification, feature representation, forecasting, and/or data generation, Neural Networks are adept at untangling complex, real-world problems. Useful for modeling both structured and unstructured data types, Neural Networks have contributed to recent breakthroughs in industries spanning across finance, healthcare, the life sciences, climatology, video remastering, natural language processing, and business analytics for decision-making, modeling, and generative purposes.
Focusing on the Keras API with TensorFlow, this Hands-on-Workshop equips attendees with the skills and knowledge necessary to understand Deep Learning fundamentals in Python. By developing, training, and evaluating their own Artificial and Convolutional Neural Networks, attendees will better understand the potential applications, limitations, and implications of adopting Deep Learning methodologies into their organizations' existing (or new) AI workflows.
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