Everything a .NET Developer Wanted to Know About Deep Learning but Was Afraid to Ask
summary
An overview of common neural network types and how .NET developers can use pretrained models and fine-tune them for real tasks, with a focus on image processing (object recognition and detection). The talk demonstrates using ML.NET to load and adapt existing networks, showing practical patterns for integrating deep learning into .NET projects despite the ecosystem’s Python dominance.
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chapters (10)
- 0:00Introduction
- 3:44What are neural networks
- 10:09Build a simple neural network
- 15:35Training with TensorFlow
- 24:00Multilayer neural networks
- 33:04Convolutional neural networks
- 47:17Training large networks
- 56:11Transfer learning
- 1:00:05Object detection
- 1:03:39Q&A
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