2018/1/11 Tech feeds highlight:

From KDNuggets:

tensorflow customization: Introduce PowerSign/AddSign as better choice of optimizer than Adam when using on recurrent neural network and show how to customize a tensorflow optimizer:

basic optimizer consisting of two major methods: compute_gradients and apply_gradients

customization is done with apply_gradients method by adding a new Python class with the following methods (very similar to extending Theano):

_create_slots(): create memory slots for variables, taking a var_list as input

_apply_dense(): define how to update variables

_apply_sparse(): same as _apply_dense() but a sparse version

_prepare(): convert python variables to tensor finally using Rosenbrock function to test on optimizer’s performance

lasso & ridge explanation: Have a nice picture illustrating the difference of lasso & ridge

docker primer: Provides the reasons for using docker: reproductivity, portability and deployment (can release as a data App). Its content covers the basic use of docker from installation to building own docker image (mainly use ubuntu platform). Another docker tutorial

From Google research:

introduce the image compression challenge in CVPR 2018: compressed images works for mobile-scale training but lose information during compression. To maintain a best quality in the compressed images requires machine-learning skill (mainly a neural network).

jobs & other non-technical topcis:

study on data scientist profiles: doesn't have that much insight

what’s like being a data scientist: Part 1, Part 2, Part 3

TED talk about AI (most of them are social ethical issues)