Visão geral do processo de planejamento financeiro e processo de controle

 

Algebraic and transcendental numbers

 

Euclid’s algorithm

 

The Toyota Production System on Software

Nonstock Production

Just-in-Time flow means eliminating the stockpiles of in-process inventory that used to be made in the name of economies of scale. The focus is on making everything in small batches, and in order to do this, it is necessary to be able to changeover a machine from making one part to making a different part very quickly. In software development, one way to look at set-up time is to consider the time it takes to deploy software. Some organizations take weeks and months to deploy new software, and because of this they put as many features into a release as possible. This gives them a large batch of testing, training, and integration work to do for each release. On the other hand, I expect the antivirus software on my computer to be updated with a well-tested release within hours after a new threat is discovered. The change will be small, so integration and training are generally not a concern.

Zero Inspection

The idea behind autonomation is that a system must be designed to be mistake-proof. There should not be someone looking for a machine to break or testing product to see if it is good. A properly mistake-proofed system will not need inspection. My video cable is an example of mistake-proofing. I can't plug a monitor cable into a computer or video projector upside down because the cable and plug are keyed. So I don't need someone to inspect that I plugged the cable in correctly, because it's impossible to get it wrong. Mistake-proofing assumes that any mistake that can be made will eventually be made, so take the time at the start to make the mistake impossible.

 

The only way to go fast, is to go well.

 

Agile

 

Time-series modeling and forecasting

 

The place of feature engineering in the machine learning workflow

 

The teleological representation for actions

 

A schematic for the typical modeling process

 

Plato’s Myth of Cave

 

TEN INTERESTING FACTS ABOUT THE HUMAN BRAIN

1. There are 86 billion neurons in the human brain (Azevedo et al., 2009).
2. Each neuron may connect with around 10,000 other neurons.
3. If each neuron connected with every single other neuron, our brain would be 12.5 miles in diameter (Nelson & Bower, 1990). This is the length of Manhattan Island. This leads to an important conclusion—namely, that neurons only connect with a small subset of other neurons. Neurons may tend to communicate only with their neighbors, and long-range connections are the exception rather than the rule.
4. The idea that we only use 10 percent of the cells in our brain is generally considered a myth (Beyerstein, 1999). It used to be thought that only around 10 percent of the cells in the brain were neurons (the rest being cells called glia), hence a plausible origin for the myth. This “fact” also turns out to be inaccurate, with the true ratio of neurons to glia being closer to 1:1 (Azevedo et al., 2009). Glia serve a number of essential support functions; for example, they are involved in tissue repair and in the formation of myelin.
5. The brain makes up only 2 percent of body weight.
6. It is no longer believed that neurons in the brain are incapable of being regenerated. It was once widely believed that we are born with our full complement of neurons and that new neurons are not generated. This idea is now untenable, at least in a region called the dentate gyrus (for a review, see Gross, 2000).
7. On average, we lose a net amount of one cortical neuron per second. A study has shown that around 10 percent of our cortical neurons perish between the ages of 20 and 90 years— equivalent to 85,000 neurons per day (Pakkenberg & Gundersen, 1997).
8. Identical twins do not have anatomically identical brains. A comparison of identical and nonidentical twins suggests that the three-dimensional cortical gyral pattern is determined primarily by non-genetic factors, although brain size is strongly heritable (Bartley et al., 1997).
9. People with autism have large brains (Abell et al., 1999). They also have large heads to accommodate them. There is unlikely to be a simple relationship between brain size and intellect (most people with autism have low IQ), and brain efficiency may be unrelated to size.
10. Men have larger brains than women, but the female brain is more folded, implying an increase in surface area that may offset any size difference (Luders et al., 2004). The total number of cortical neurons is related to gender, but not overall height or weight (Pakkenberg & Gundersen, 1997).

Processo de Construção de um Intervalo de Confiança para µ

 

Confidence Intervals from 20 Different Samples (95%)

 

How Independent Component Analysis Works

 

Comparison between principal component analysis (PCA) and independent component analysis (ICA).  The coordinates of each dot are their values at a single time point. PCA is a popular unsupervised learning technique that picks out a direction that bisects the two signals, maximally mixing them, and the PCA axes are always perpendicular to each other. ICA finds the axes that fall along the directions of the dots, representing the separated signals, which may not be perpendicular.

Overfitting

 

Geometric explanation for how two object categories are discriminated by a perceptron.

The objects have two features, such as size and brightness, which have values

(x,y) and are plotted on each graph. The two types of objects (pluses and squares) in

the panel on the left can be separated by a straight line that passes between them;

this discrimination can be learned by a perceptron. The two types of objects in the

other two panels cannot be separated by a straight line, but those in the center panel

can be separated by a curved line. The objects in the panel on the right would have to

be gerrymandered to separate the two types. The discriminations in all three panels

could be learned by a deep learning network if enough training data were available.

Fermat’s Last Theorem

 

Scrum Framework

 

The Seven Wastes

 

Some Books

• Drive: The Surprising Truth About What Motivates Us
• The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth
• The Culture Code: The Secrets of Highly Successful Groups
• Scaling Agile @ Spotify, with Tribes, Squads, Chapters & Guilds
• The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win 
• Implementing Lean Software Development: From Concept to Cash 
• User Story Mapping: Discover the Whole Story, Build the Right Product 
• Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations 
• Escaping the Build Trap: How Effective Product Management Creates Real Value

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