Introduction to Deep Learning
summary
Overview of deep learning contrasted with traditional machine learning techniques, explaining neural networks and how GPUs are used to accelerate them. Presents a practical use case: traffic sign signal classification, tying the concepts together with that example.
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chapters (22)
- 0:00Introduction
- 1:03Can I find the car using rules?
- 2:18What can we do with Machine Learning?
- 3:24What is this Machine Learning thing?
- 6:15And where does Deep Learning fit in?
- 7:25What is the fundamental difference?
- 11:10What exactly is image classification?
- 12:24And where can we use it?
- 13:45Why Neural Networks?
- 14:26Say hello to an Artificial Neuron
- 14:58Neural Network
- 17:55How are they trained? Forward Prop.
- 18:38How are they trained? Back Propagation
- 19:40More realistic error function
- 20:25Common Neural Network architectures
- 22:21Is a CPU or GPU better?
- 23:25Why GPU & Deep Learning?
- 25:27The Nature Conservancy Fisheries Monitoring
- 27:29Problem: unbalanced dataset
- 28:25Data Augmentation
- 29:52Architecture: CNN+FC
- 30:51Things to remember!
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