The bigger model is not always the better model. The Next AI Revolution. The ability to run machine learning models on resource-constrained devices opens up doors to many new possibilities.
Miniaturization of electronics started by NASA’s push became an entire consumer products industry. Now we’re carrying the complete works of Beethoven on a lapel pin listening to it in headphones. —_ Neil deGrasse Tyson, astrophysicist and science commentator_
[…] the pervasiveness of ultra-low-power embedded devices, coupled with the introduction of embedded machine learning frameworks like TensorFlow Lite for Microcontrollers will enable the mass proliferation of AI-powered IoT devices. — _**_Vijay Janapa Reddi, Associate Professor at Harvard University**
Overview of tiny machine learning (TinyML) with embedded devices.
This is the first in a series of articles on tiny machine learning. The goal of this article is to introduce the reader to the idea of tiny machine learning and its future potential. In-depth discussion of specific applications, implementations, and tutorials will follow in subsequent articles in the series.
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Artificial Neural Networks — Recurrent Neural Networks. Remembering the history and predicting the future with neural networks. A intuition behind Recurrent neural networks.