SharpLance

 Ever wondered what kind of CPU model you are using in Linux? This command will pull it up: gcc -march=native -Q --help=target|grep march

 https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/  Source: Udacity AI for Business Leaders Supervised Learning - A category of machine learning which relies on the proper values, or labels , being present for the output data in the ground truth dataset, which the model can “learn” from during its training process. Labels - Values of the output variable/column/piece of data you are interested in Unsupervised Learning - A category of machine learning which uses the underlying characteristics of data itself, rather than already-supplied labels for outputs, to inform the model’s training process. Often uses segmenting or clustering algorithms to determine the output values. Reinforcement Learning - A category of machine learning which uses a series of simulations/cycles to reward optimal behaviors to learn policies for decision making in complex scenarios such as autonomous driving.

 Data: Volume - the amount of data that is being produced over any given unit of time Variety - The level of deviation within your data, which can have both positive and negative effects depending on what it is you’re hoping to achieve Velocity - A term referring to how quickly new data is produced. Velocity can also allude to the concept of drift, or, how quickly data underlying a model can change over time Veracity - The accuracy of data that is being collected, a trait which can be affected by faulty inputs, poor organization, or a variety of other factors Value - A holistic measure based on all other underlying characteristics of data and rooted in how likely the data is to help you reach your desired end state Differences between AI, ML, and DL?  NVIDIA: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ Source: Udacity AI for Business Leaders

Excellent summary. https://arghya.xyz/articles/webassembly-wasm-wasi/

If doing Fedora network install and booted without Gnome, this might help (seen at r/fedora): First elevate yourself into sudo using: sudo su Then use the following commands: dnf groupinstall gnome-desktop dnf groupinstall base-x systemctl set-default graphical.target Systemctl enable gdm.service Reboot

Mere notes Model: A mathematical representation of a problem, situation, phenomenon, or process Training: The process of using an algorithm to create a model from a set of data Training Set: A subset of our ground truth data that our model will learn from Algorithm: A procedure, or set of steps Machine learning (probabilistic approach) vs Rules-based system If the universe of possible outcomes is well delineated then stick with rules. Purpose of Ground Truth Data Ground truth data helps many learning systems to learn and evaluate their own performance by providing a gold standard on what the truth actually is. Source: Udacity AI for Business Leaders