Deep Learning Revolution: Rethinking Machine Learning Pipelines
summary
Deep learning replaces handcrafted features with end-to-end models that learn from raw data, using GPU acceleration (NVIDIA changes favor float precision) and frameworks like Torch and Caffe. ImageNet error falls from ~26% with old features to a ~10% jump from early deep nets and down to ~4.5% near human (~3–5%) performance. Training goes from months on CPUs to weeks on GPUs and some models train in 3–4 hours; transfer learning and data augmentation cut required labels.
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chapters (26)
- 0:00Start
- 1:07Getting your attention
- 17:40Hand-Crafted Features
- 17:59Traditional ML
- 19:26Deep learning
- 21:00How to win at life (5-step process)
- 22:01What is a neural network?
- 22:53What is a deep neural network?
- 24:01Neural networks vs computation graphs?
- 29:04Locally connected layers
- 30:11Convolutional neural networks
- 32:54ImageNet classification results 2012
- 34:50Segmentation and Stereo
- 36:04Generative Adversarial Nets
- 36:45ConvNets for Video
- 37:11ConvNets for NLP
- 40:35Recurrent Networks
- 41:29Acyclic computational graphs
- 41:46RNN-LSTMs
- 43:45Examples
- 45:15Recurrent Nets for Q&A
- 46:15Implementation: FLOP Eaters
- 48:59Training
- 50:22Frameworks
- 51:48Trends
- 52:46Challenges
