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Greening the Economy: Sustainable Cities
Introduction to Graphic Illustration
Computational Social Science Methods
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Explore how data science transforms social media data, revealing insights to optimize profits for high-revenue properties. Learn about AI model building, applications, failures, bias, and solutions.
Learn best practices for building ML data labeling pipelines through crowdsourcing. Explore real-life examples, requirements, and techniques for high-quality labeled data in supervised machine learning projects.
Explore vision transformers' architecture, self-attention in computer vision, and applications in recognition, segmentation, and multi-modal learning. Discover their potential for general-purpose model architectures.
Explore AI's impact on cybersecurity, its potential risks and benefits, and strategies for ensuring AI safety and ethical development in this thought-provoking talk by Dr. Yampolskiy.
Learn to build robust data pipelines using dbt, Airflow, and Great Expectations. Discover how these tools complement each other to ensure data quality, perform transformations, and orchestrate workflows effectively.
Explore responsible AI use in healthcare, addressing challenges, identifying biases, and implementing ethical practices to enhance patient outcomes and promote health equity.
Explore Bayesian statistical computation and Hamiltonian Monte Carlo algorithms using PyMC3, an open-source probabilistic programming package. Learn to apply these advanced techniques to complex models in Python.
Explore deep learning survival analysis for precise credit risk prediction in consumer lending, combining traditional methods with advanced techniques for improved time-to-event forecasting.
Explore vector search in data science with Connor Shorten, covering Weaviate Vector Search Engine, healthcare applications, and content performance analytics. Gain insights into vector representations and segmentation.
Explore inclusive AI in personalized discovery systems, focusing on fairness, diversity, and privacy. Learn strategies for reducing bias and implementing responsible AI in search and recommendation algorithms.
Explore scaling machine learning with Data Mesh, covering key concepts like data as a product, self-serve infrastructure, and federated governance. Learn innovative strategies for enhancing ML capabilities and driving business value.
Explore data-centric AI with Alex Ratner: operationalizing knowledge, managing training data, and leveraging foundational models for real-world applications in machine learning and responsible AI.
Explore social biases in text representations and their mitigation with NLP expert Danushka Bollegala. Learn about gender bias, word embeddings, masked language models, and multi-lingual bias evaluation in AI systems.
Explore engineering best practices for data lakes architectures, covering data infrastructure evolution, building blocks of data products, and practical tooling examples for effective data engineering.
Explore data engineering career paths, industry changes, and essential skills with Joe Reis. Gain insights on transitioning from software engineering or data analysis roles and receive valuable career advice.
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