ML Foundations for AI Engineers (in 34 Minutes)

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