Note: the complete collection of Part 2 video timelines is available in a single thread for keyword search.
Part 2: complete collection of video timelines
Lesson 11 video timeline:
00:00:30 Tips on using notebooks and reading research papers
00:03:15 Follow-up on lesson 10 and more word-to-image searches
00:07:30 Linear algebra cheat sheet for deep learning (student’s post on Medium)
& Zero-Shot Learning by Convex Combination of Semantinc Embeddings (arXiv)
00:10:00 Systematic evaluation of CNN advances on ImageNet (arXiv)
ELU better than RELU, learning rate annealing, different color transformations,
Max pooling vs Average pooling, learning rate & batch size, design patterns.
00:27:15 Data Science Bowl 2017 (Cancer Diagnosis) on Kaggle
00:36:30 DSB 2017: full preprocessing tutorial, + others.
00:48:30 A non-deep-learning approach to find lung nodules (research)
00:53:00 Clustering (and why Jeremy wasn’t a fan before)
01:08:00 Using Pytorch with GPU for ‘meanshift’ (clustering cont.)
01:22:15 Candidate Generation and LUNA 16 (Kaggle)
01:26:30 Accelerating K-Means on GPU via CUDA (research)
01:27:15 ChatBots ! (long section)
Starting with “memory networks” at Facebook (research)
01:57:30 Recurrent Entity Networks: an exciting area of research in Memory Networks
01:58:45 Concept of “Attention” and “Attentional Models”