30 AI Projects You Can Build This Weekend: https://the-data-entrepreneurs.kit.com/30-ai-projects
Modern AI is built on ML. Although builders can go far without understanding its details, they inevitably hit a technical wall. In this guide, I cover the ML essentials that engineers need to know.
📰 Read more: https://medium.com/data-science-collective/ml-foundations-for-ai-engineers-bda353152d24?sk=7efbf8a574c117044a41b288ddbccc14
References
[1] https://youtu.be/alfdI7S6wCY
[2] The Royal Society. Machine Learning: The Power and Promise of Computers That Learn by Example. The Royal Society, 2017. https://royalsociety.org/~/media/policy/projects/machine-learning/publications/machine-learning-report.pdf
[3] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “ImageNet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems, vol. 25, 2012, pp. 1097–1105.
[4] Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016). https://doi.org/10.1038/nature16961
[5] Williams, R.J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8, 229–256 (1992). https://doi.org/10.1007/BF00992696
Introduction – 0:00
Intelligence & Models – 0:40
3 Ways Computers Can Learn – 1:50
Way 1: Machine Learning – 2:47
Inference (Phase 2) – 3:36
Training (Phase 1) – 4:27
More ML Techniques – 9:07
Way 2: Deep Learning – 10:43
Neural Networks – 12:06
Training Neural Nets – 15:29
Way 3: Reinforcement Learning (RL) – 21:56
The Promise of RL – 23:25
How RL Works – 25:16
Data (most important part!) – 30:30
Key Takeaways – 33:32
Homepage: https://www.shawhintalebi.com
source