Helping Business Leaders Get Over Their Learning Curve in Advanced Analytics
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The document discusses some of the challenges business leaders face when adopting advanced analytics, including determining return on investment, trusting technology to make decisions, and managing organizational changes. It notes that ROI can come from both cost savings and revenue generation use cases, but there may be hidden project and lifecycle costs. Advanced analytics requires descriptive, predictive, and prescriptive models to build trust. Finally, the document compares centralizing analytics under a Chief Data Officer versus keeping functions decentralized, noting pros and cons of each approach including potential hidden costs of organizational changes or data management challenges.
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Why Cisco- for-Intelligence
#ESGJRConsultingInc #Software #Cisco #Network #Engineering #CNSVitalSigns #DNAIDSmartCard
Cisco Certifications
Go to www.esgjrconsultinginc.com to learn more about Software/Network Engineering Projects.
Building Data Science Teams
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Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
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FRS, FACM, FIEEE; DISTINGUISHED PROFESSOR OF COMPUTER SCIENCE, UCLA; CO-FOUNDER & CHIEF SCIENTIST, VOXELCLOUD INC.Generating Creative Works with AI
This document discusses using generative AI models for creative work such as generating movie scripts and content. It describes several generative models including restricted Boltzmann machines, deep generative models, variational autoencoders, and hierarchical LSTMs. It proposes using these models to help optimize the movie production process by automatically generating schedules, budgets, storyboards, and dialog from input scripts to help filmmakers explore creative options. An example project of a movie conceived by humans but with AI-generated dialog is briefly described.
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Using AI to Tackle the Future of Health Care DataIDEAS - Int'l Data Engineering and Science Association
This document discusses using artificial intelligence and graph analytics to tackle challenges in healthcare data and clinical trials. It notes that patient data is difficult to analyze due to its complexity and size, and that new sources of healthcare data are emerging but not being fully utilized. It argues that a graph-based approach could help reveal hidden relationships in the data to improve patient matching for clinical trials and other applications. However, existing tools may not be suitable due to limitations in scalability, flexibility or focus on performance over data. Next steps proposed include developing novel infrastructure combining high performance computing techniques with modern big data tools like Spark and TensorFlow to enable graph analytics at scale.State of AI/ML in Real Estate
This document discusses the state of AI/ML in real estate. It notes that the US real estate market alone is over $1.5 trillion annually with over 5 million homes sold per year. Popular approaches to real estate valuation include automated valuation models using regressions, decision trees or neural networks, comparative market analysis of similar homes, and professional appraisals. The document also discusses sources of real estate data like MLS listings and public records and evaluates a catboost model for home price prediction against other popular models, finding it outperforms them on several metrics. It concludes there is significant opportunity for disruption in real estate analytics through better use of data science approaches and contextual data.
Hot Dog, Not Hot Dog! Generate new training data without taking more photos.
Hot Dog, Not Hot Dog! Generate new training data without taking more photos.IDEAS - Int'l Data Engineering and Science Association
This document discusses improving object recognition models through generating additional training data. It notes that while current models work well, they need more diverse training examples to handle real-world scenarios. The document suggests techniques like image rotations, noise/blurring, and brightness adjustments to artificially expand training datasets without capturing new photos. It emphasizes generating variations during training to save disk space and time. The overall message is that more robust training data is key to developing powerful computer vision models.More from IDEAS - Int'l Data Engineering and Science Association (20)
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