Applying Convolutional Neural Networks with TensorFlow

In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network. All these concepts will be brought to life by demonstrating how Databricks simplifies deep learning - letting you quickly access ready-to-use ML environments, as well as prepare data, and train models faster. After this session, if requested, you will receive the presentation and associated notebooks so you can run the samples yourself.
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Spotlight

OTHER ON-DEMAND WEBINARS

Moving from centralized data platform to a federated data mesh

In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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Common Mistakes to avoid when implementing your Data Governance Program

Are you planning to implement a data governance program? What are the things that practitioners often overlook or underestimate, and what are the most common mistakes they make? Join us to hear from Katherine Fraser, CEO of 1 to learn more about the key components of a successful data governance program, common mistakes made and to avoid when implementing a data governance program, and tips for maintaining a successful data governance program.
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Modernizing Metadata

tdwi.org

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.
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Approaching Data Management Technologies

infogix

Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of Data Management technologies that can be used to increase the productivity of Data Management efforts.
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