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 13 video timeline:
00:00:10 Fast.ai student accepted into Google Brain Residency program
00:06:30 Cyclical Learning Rates for Training Neural Networks (another student’s paper)
& updates on Style Transfer, GAN, and Mean Shift Clustering research papers
00:13:45 Tiramisu: combining Mean Shitft Clustering and Approximate Nearest Neighbors
00:22:15 Facebook AI Similarity Search (FAISS)
00:28:15 The BiLSTM Hegemony
00:35:00 Implementing the BiLSTM, and Grammar as a Foreign Language (research)
00:45:30 Reminder on how RNN’s work from Lesson #5 (Part 1)
00:47:20 Why Attentional Models use “such” a simple architecture
& “Tacotron: a Fully End-To-End Text-To-Speech Synthesis Model” (research)
00:50:15 Continuing on Spelling_bee_RNN notebook (Attention Model), from Lesson 12
00:58:40 Building the Attention Layer and the ‘attention_wrapper.py’ walk-through
01:15:40 Impressive student’s experiment with different mathematical technique on Style Transfer
01:18:00 Translate English into French, with Pytorch
01:31:20 Translate English into French: using Keras to prepare the data
Note: Pytorch latest version now supports Broadcasting
01:38:50 Writing and running the ‘Train & Test’ code with Pytorch
01:44:00 NLP Programming Tutorial, by Graham Neubig (NAIST)
01:48:25 Question: “Could we translate Chinese to English with that technique ?”
& new technique: Neural Machine Translation of Rare Words with Subword Units (Research)
01:54:45 Leaving Translation aside and moving to Image Segmentation,
with the “The 100 layers Tiramisu: Fully Convolutional DenseNets” (research)
and “Densely Connected Convolutional Networks” (research)