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

Machine Learning is a branch that focuses on probabilistic reasoning.  Progress in artificial intelligence progressed due to advances in Machine Learning. Deep learning is a technique of applying Machine Learning with massive neural networks. Good source:  https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Sebastain Thrun: Artificial Intelligence represents an opportunity for lessons learned to be applied on all systems and including systems not made yet. Business leaders often want to go a certain direction but available toolsets including education did not keep up. Anything repetitive would likely be done by AI within 20 years.  Erik Brynjolfsson. Director, MIT Initative on Digital Economy: Book: The Second Machine Age Three trends: Power Data Algorithms Machines with very narrow specialty. Machine learning - when to use and when not to use. Find the problem then find the solution. Identify the questions. Image recognition - machines are way better. Lots of churn under the surface so you have to be working on opportunities even though it looks like nothing is happening. Examples: A team looked flight patterns and was able to predict merger and acquisitions.  A team used logs from locomotives to identify improvements to be made. Finance showed massive improvements - 360,000 hou...

 https://www.build-electronic-circuits.com/kicad-tutorial/  This is a great tutorial.