uTensor
uTensor is an extremely light-weight machine learning inference framework built on Mbed and Tensorflow. It consists of a runtime library and an offline tool. The total size of graph definition and algorithm implementation of a 3-layer MLP produced by uTensor is less than 32kB in the resulting binary (excluding the weights).
Simple Neural Network on MCUs …

Artificial General Intelligence (AGI)
Artificial general intelligence (AGI) is the intelligence of a (hypothetical) machine that could successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and an important topic for science fiction writers and futurists. Artificial general intelligence is also referred to as “strong AI”, “full AI” or as the ability to perform “general intelligent action”. AGI is associated with traits such as consciousness, sentience, sapience, and self-awareness observed in living beings. …

Dynamic Autoencoder (DynAE)
In unsupervised learning, there is no obvious straightforward loss function which can capture the major factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised loss function remains static during the training process. The absence of concrete supervision suggests that smooth complex dynamics should be integrated as a substitute to the classical static loss functions to better make use of the gradual and uncertain knowledge acquired through self-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a new model for deep clustering that allows to solve a clustering-reconstruction trade-off by gradually and smoothly eliminating the reconstruction objective in favor of a construction one while preserving the space topology. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to all the other autoencoder-based clustering methods. …

Dask
Dask is a flexible parallel computing library for analytic computing. Dask is composed of two components:
1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads.
2. ‘Big Data’ collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.
Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python) …