Introduction To Deep Learning
summary
Deep learning here means artificial neural networks with more than one hidden layer that consistently outperform classic ML on large datasets for images, text and audio; training uses backprop, CNNs for vision and LSTMs for sequence tasks, and benefits from 300 million images showing no clear performance plateau. GPUs give roughly a 50x GPU speedup over CPUs for training, and very deep models (a 152-layer network on ImageNet) beat humans; transfer learning and cloud Volta GPUs reduce data and compute barriers, but adversarial examples still break models.
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chapters (25)
- 0:00Introduction and speaker background
- 1:06What is deep learning
- 2:50Machine learning vs rule-based systems
- 6:08Classic machine learning limitations
- 8:15Deep learning advantages
- 11:33History of AI and deep learning
- 14:12Data's role in deep learning breakthroughs
- 16:45Breakthroughs in computer vision
- 19:16Deep learning limitations and challenges
- 20:25Key factors behind deep learning success
- 21:50GPUs and compute power in deep learning
- 23:16Deep learning applications today
- 24:18Emerging text and NLP applications
- 25:48Multimodal deep learning future
- 26:53Engineering neural network architectures
- 27:57Barriers to entry in deep learning
- 29:45Overcoming compute power and expertise limits
- 30:50How neural networks work
- 34:47Forward and backward propagation
- 36:38Neural network layers and efficiency
- 37:46Convolutional neural networks
- 38:30Recurrent neural networks and LSTMs
- 41:01Deep learning use case examples
- 41:46Conclusion and resources
- 42:13Q&A session
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