Author: Vincent Granville
Now published. Enterprise AI: An applications perspective takes a use case driven approach to understand the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a practical and straightforward roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk’s new course on Implementing Enterprise AI.
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- Machine Learning, Deep Learning and AI
- The Data Science Process
- Categories of Machine Learning algorithms
- How to learn rules from Data?
- An introduction to Deep Learning
- What problem does Deep Learning address?
- How Deep Learning Drives Artificial Intelligence
Deep Learning and neural networks
- Perceptrons – an artificial neuron
- MLP – How do Neural networks make a prediction?
- Spatial considerations – Convolutional Neural Networks
- Temporal considerations – RNN/LSTM
- The significance of Deep Learning
- Deep Learning provides better representation for understanding the world
- Deep Learning a Universal Function Approximator
What functionality does AI enable for the Enterprise?
- Technical capabilities of AI
- Functionality enabled by AI in the Enterprise value chain
Enterprise AI applications
- Creating a business case for Enterprise AI
- Four Quadrants of the Enterprise AI business case
- Types of AI problems
Enterprise AI – Deployment considerations
- A methodology to deploy AI applications in the Enterprise
- DevOps and the CI/CD philosophy
- Invitation to Join Data Science Central
- Free eBook: Enterprise AI – An Applications Perspective
- Free Book: Applied Stochastic Processes
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
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