Haiku Tech Center

 This is a talk given by Mathias Niessner at MIT in May 2020.

 This is a discussion between the R&D Director and the principal Software Engineer at the Qt Company that contrasts C++ widgets vs QML controls for building desktop applications.

 This overview tutorial is from the Qt Virtual tech Conference in 2020.  It provides a good overview of what is going on under the hood in Qt 6, and how that was changed from what was going on in Qt 5.14 and 5.15. Here's a followup from fall 2020 to help out understanding how all of this comes together in Qt 6. Redirecting Qt Quick rendering QQuickRenderControl API QQuickRenderTarget Shaders CMake in build system converts .frag files to .qsb files for inclusion in the application

Interesting paper out of Google that looks at using a neural net to optimize image resizing for images that are going to be processed by a neural net classifier system.   The results might see surprising at first, but remember, the resized images are for the purpose of improving the classification accuracy of the system, not necessarily for human viewing.  You can check out the paper here .

 This is a workshop Google put together last week associated with Google IO. How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological strides in recent decades, there is an unsettling feeling of a lack of “conceptual” understanding of why it works and to what extent it will work in the current form. The goal of the workshop is to bring together theorists and practitioners to develop an understanding of the right algorithmic view of deep learning, characterizing the class of functions that can be learned, coming up with the right learning architecture that may (provably) learn multiple functions, concepts and remember them over time as humans do, theoretical understanding of language, logic, RL, meta learning and lifelong learning.

This is a Turing Lecture by Stephane Mallat presented at the Alan Turing Institute in 2016. Observations 1:  Learning invariance to deformation. 2:  Classification of stationary textures. 3:  Multi-scale separation. 4: The role of channel connections in building invariance in CNN.

 Fresh from Google IO 2021 is this overview of what is new in TensorFlow. Let's check out a presentation on Modern Keras Design Patterns.