Why Machine Learning? Can you write code that tells the difference between an apple and an orange?
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You could write manual rules...
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Creating manual rules requires lots of code
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Machine Learning learns from examples
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Workflow: 3 steps to train a ML model
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ML can solve a variety of mission critical business problems
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How to draw a Machine Learning model
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Instacart value proposition
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Four sided marketplace
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Customer experience
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Personal shopper experience
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Encoding
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New products
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Competitive products
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Recommended products
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Unsupervised learning
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Study the differences
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Picking groceries
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Let's get store data!
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Traveling salesman problem
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Results
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Problem definition
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Tensorflow architecture
Description:
Explore machine learning and AI applications in app development through this 29-minute conference talk from Playtime San Francisco 2017. Discover real-world examples of developers successfully leveraging these technologies to enhance app and game performance. Dive deep into Instacart's case study, examining how they utilize machine learning for various aspects, including logistics and growth. Learn about the three-step workflow for training ML models, understand the value of machine learning in solving complex business problems, and gain insights into encoding techniques for product recommendations. Examine the challenges of the traveling salesman problem in the context of grocery picking, and get an introduction to TensorFlow architecture. Presented by Kevin Fives from Google Play and Montana Low from Instacart, this talk provides valuable insights for developers looking to integrate AI and machine learning into their applications.
Self-Driving Apps - Using Machine Learning and AI to Improve App Performance