Effect of techniques from Fast.ai

fast.ai is a brilliant library and a course by Jeremy Howard an co. They use pytorch as a base and explain deep learning from the foundations to a very decent level. In his course Jeremy Howard demonstrates a lot of interesting techniques that he finds in papers and that do NN training faster/better/cheaper. Here I want to reproduce some of the techniques in order to understand what is the effect they bring....

November 15, 2020 · SergeM

Multistage NN training experiment

Ideas for multistage NN training. There is some research on continuous learning without catastrophic forgetting . For example ANML: Learning to Continually Learn (ECAI 2020) arxiv code video The code for the paper is based on another one: OML (Online-aware Meta-learning) ~ NeurIPS19 code video OML paper derives some code from MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks pdf official tf code, also includes some links to other implementations....

January 1, 2020 · SergeM

deep learning

Rest API example for tensorflow. It works: demo Trained models for tensorflow TF-slim - high-level API of TensorFlow for defining, training and evaluating complex models. Doesn’t work for python 3 (see here) VGG16 and VGG19 in Tensorflow. One more here. And one more Deep learning for lazybones Inception-like CNN model based on 1d convolutions http://arxiv.org/pdf/1512.00567v3.pdf Chat (in russian) http://closedcircles.com/?invite=99b1ac08509c560137b2e3c54d4398b0fa4c175e

June 3, 2016 · SergeM