AI for Every Developer
summary
Claims that AI is now accessible to every developer and demonstrates concrete paths: using cloud Cognitive Services and custom vision, exporting and running models via ONNX and Windows ML, or training with CNTK/Python or ML.NET in Visual Studio. Cites the 2012 cat experiment—60,000 machines, 1 billion connections, 10 million YouTube thumbnails—and transistor growth (1995→2017) to justify compute availability. Builds and trains MNIST and Iris models, exports ONNX, and embeds a C# app that evaluates handwritten digits (works but sometimes misclassifies).
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chapters (14)
- 0:00Introduction to AI
- 2:11Defining AI, ML, and DL
- 5:00Computational Power Growth
- 7:17AI Applications
- 9:09Machine Learning Approaches
- 11:26Deep Learning Fundamentals
- 12:04Implementing AI Services
- 16:18Windows ML Integration
- 19:40The AI Development Process
- 22:19Training Techniques
- 26:40Neural Network Architecture
- 30:56Advanced Model Training
- 36:42MNIST Database Projects
- 42:46Coding First AI Applications
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