After quite some research for a good dataset with handwriting, I decided to create my own, since most sets were not very good (for my purpose). I created a little GUI to draw letter by hand, and give them a label. After doing some research in Jupyter Notebooks, I kind of liked the results and edited the GUI to test the model. It works quite well for me I’d say.

Images that are misclassified can be labelled. These get added to the dataset, and the model is immediatly refitted to improve performance.

Maybe have a look for yourself here, feedback is always helpful!

we are hosting a live session with the author of the paper “private computing in tensorflow” at lunch time EST. See more details here: https://www.eventbrite.ca/e/private-machine-learning-in-tensorflow-aisc-lunch-learn-tickets-64991077061

what questions do you have about this paper that we can ask the author?

abstract: We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and practitioners, including tight integration with ordinary machine learning processes, existing optimizations for distributed computation in TensorFlow, high-level abstractions for expressing complex algorithms and protocols, and an expanded set of familiar tooling. We give an open source implementation of a state-of-the-art protocol and report on concrete benchmarks using typical models from private machine learning.

Right now, I’m building a model that’s trying to classify car images according to their models. My problem with the current model is that it’s performing badly on certain classes. To elaborate further, there are two classes that are very similar and the model seems to mis-classify one of the classes into the other one, but not the other way around (A mostly classified as B while B is classified as B). I don’t think it is caused by uneven data distribution because when i perform the training, I ensure that all classes have more or less the same number of samples. Would anyone have any suggestions to solve this? Any idea would be great. Thanks!

Blog post by Author: https://carl-allen.github.io/nlp/2019/07/01/explaining-analogies-explained.html

Paper: https://arxiv.org/abs/1901.09813

Towards Data Science Post: link

Arxiv link: https://arxiv.org/abs/1906.08237

We are proud to share our source code for “Learnable Gated Temporal Shift Module for Deep Video Inpainting. Chang et al. BMVC 2019.” We developed a simple module to reduce training & testing time and model parameters for deep free-form video inpainting based on the Temporal Shift Module for action recognition. It achieves similarly good results as our previous work “Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN. Chang et al. arXiv 2019.” with only 33% of parameters.

GitHub: https://github.com/amjltc295/Free-Form-Video-Inpainting

Arxiv: https://arxiv.org/abs/1907.01131

Contributions and stars are welcome!

I started to learn machine learning from Andrew Ng’s course. I knew the course doesn’t teach the math behind comprehensively. I started to study statistical learning at the same time. At the beginning it helped me to understand the concepts and math behind the ideas we are introduced. But now it’s like two of them approaching to same problem from different sides. Should I continue to study statistical learning in this case? Would you have any recommendations? which resource should I use to learn the math behind?