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Annotation 1409645939980

#aviation #wiki

An airplane or aeroplane (informally plane) is a powered , fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller .

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Airplane - Wikipedia
magelink] [emptylink] The first flight of an airplane, the Wright Flyer on December 17, 1903 [imagelink] [emptylink] An All Nippon Airways Boeing 777 -300 taking off from New York JFK Airport . An airplane or aeroplane (informally plane) is a powered , fixed-wing aircraft that is propelled forward by thrust from a jet engine , propeller or rocket engine . Airplanes come in a variety of sizes, shapes, and wing configurations . The broad spectrum of uses for airplanes includes recreation , transportation of goods and people, military , and

Flashcard 1409649085708

Tags

#aviation #wiki

Question

An airplane or aeroplane (informally plane) is a powered , fixed-wing aircraft that is propelled forward by thrust from a [...] or propeller .

Answer

jet engine

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An airplane or aeroplane A (informally plane) is a powered, fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller.

Original toplevel document

Airplane - Wikipedia
her uses, see Airplane (disambiguation) and Aeroplane (disambiguation). [imagelink] North American P-51 Mustang, a World War II fighter [imagelink] The first flight of an airplane, the Wright Flyer on December 17, 1903 An airplane or aeroplane A (informally plane) is a powered, fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller. Airplanes come in a variety of sizes, shapes, and wing configurations. The broad spectrum of uses for airplanes includes recreation, transportation of goods and people, military, and re

Flashcard 1409650134284

Tags

#aviation #wiki

Question

An airplane or aeroplane (informally plane) is a powered , fixed-wing aircraft that is propelled forward by thrust from a jet engine or [...] .

Answer

propeller

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An airplane or aeroplane A (informally plane) is a powered, fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller.

Original toplevel document

Flashcard 1447529417996

Tags

#sister-miriam-joseph #trivium

Question

In a natural object the following are similar but distinct: [...], essence, [...], form, species.

Answer

substance

nature

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In a natural object the following are similar but distinct: substance, essence, nature, form, species.

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Flashcard 1448447708428

Tags

#cfa-level-1 #factors-that-determine-market-structures #microeconomics #reading-16-the-firm-and-market-structures #section-2-analysis-of-mkt-structures #study-session-4

Question

Exhibit 1. Characteristics of Market Structure

Market Structure	Number of Sellers	Degree of Product Differentiation	Barriers to Entry	Pricing Power of Firm	Non-price Competition
Perfect competition	Many	Homogeneous/ Standardized	[...]	None	[...]
Monopolistic competition	Many	Differentiated	Low	Some	Advertising and Product Differentiation
Oligopoly	Few	Homogeneous/ Standardized	[...]	Some or Considerable	[...]
Monopoly	One	Unique Product	Very High	Considerable	Advertising

Answer

High

None

Very Low

Advertising and Product Differentiation

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ng Power of Firm Non-price Competition Perfect competition Many Homogeneous/ Standardized Very Low None None Monopolistic competition Many Differentiated Low Some Advertising and Product Differentiation Oligopoly Few Homogeneous/ Standardized High Some or Considerable Advertising and Product Differentiation Monopoly One Unique Product Very High Considerable Advertising<body><html>

Original toplevel document

2. ANALYSIS OF MARKET STRUCTURES
d monopoly is the local electrical power provider. In most cases, the monopoly power provider is allowed to earn a normal return on its investment and prices are set by the regulatory authority to allow that return. 2.2. Factors That Determine Market Structure Five factors determine market structure: The number and relative size of firms supplying the product; The degree of product differentiation; The power of the seller over pricing decisions; The relative strength of the barriers to market entry and exit; and The degree of non-price competition. The number and relative size of firms in a market influence market structure. If there are many firms, the degree of competition increases. With fewer firms supplying a good or service, consumers are limited in their market choices. One extreme case is the monopoly market structure, with only one firm supplying a unique good or service. Another extreme is perfect competition, with many firms supplying a similar product. Finally, an example of relative size is the automobile industry, in which a small number of large international producers (e.g., Ford and Toyota) are the leaders in the global market, and a number of small companies either have market power because they are niche players (e.g., Ferrari) or have little market power because of their narrow range of models or limited geographical presence (e.g., Škoda). In the case of monopolistic competition, there are many firms providing products to the market, as with perfect competition. However, one firm’s product is differentiated in some way that makes it appear better than similar products from other firms. If a firm is successful in differentiating its product, the differentiation will provide pricing leverage. The more dissimilar the product appears, the more the market will resemble the monopoly market structure. A firm can differentiate its product through aggressive advertising campaigns; frequent styling changes; the linking of its product with other, complementary products; or a host of other methods. When the market dictates the price based on aggregate supply and demand conditions, the individual firm has no control over pricing. The typical hog farmer in Nebraska and the milk producer in Bavaria are price takers . That is, they must accept whatever price the market dictates. This is the case under the market structure of perfect competition. In the case of monopolistic competition, the success of product differentiation determines the degree with which the firm can influence price. In the case of oligopoly, there are so few firms in the market that price control becomes possible. However, the small number of firms in an oligopoly market invites complex pricing strategies. Collusion, price leadership by dominant firms, and other pricing strategies can result. The degree to which one market structure can evolve into another and the difference between potential short-run outcomes and long-run equilibrium conditions depend on the strength of the barriers to entry and the possibility that firms fail to recoup their original costs or lose money for an extended period of time and are therefore forced to exit the market. Barriers to entry can result from very large capital investment requirements, as in the case of petroleum refining. Barriers may also result from patents, as in the case of some electronic products and drug formulas. Another entry consideration is the possibility of high exit costs. For example, plants that are specific to a special line of products, such as aluminum smelting plants, are non-redeployable, and exit costs would be high without a liquid market for the firm’s assets. High exit costs deter entry and are therefore also considered barriers to entry. In the case of farming, the barriers to entry are low. Production of corn, soybeans, wheat, tomatoes, and other produce is an easy process to replicate; therefore, those are highly competitive markets. Non-price competition dominates those market structures where product differentiation is critical. Therefore, monopolistic competition relies on competitive strategies that may not include pricing changes. An example of non-price competition is product differentiation through marketing. In other circumstances, non-price competition may occur because the few firms in the market feel dependent on each other. Each firm fears retaliatory price changes that would reduce total revenue for all of the firms in the market. Because oligopoly industries have so few firms, each firm feels dependent on the pricing strategies of the others. Therefore, non-price competition becomes a dominant strategy. Exhibit 1. Characteristics of Market Structure Market Structure Number of Sellers Degree of Product Differentiation Barriers to Entry Pricing Power of Firm Non-price Competition Perfect competition Many Homogeneous/ Standardized Very Low None None Monopolistic competition Many Differentiated Low Some Advertising and Product Differentiation Oligopoly Few Homogeneous/ Standardized High Some or Considerable Advertising and Product Differentiation Monopoly One Unique Product Very High Considerable Advertising From the perspective of the owners of the firm, the most desirable market structure is that with the most control over price, because this control can lead to large profits. Monopoly and oligopoly markets offer the greatest potential control over price; monopolistic competition offers less control. Firms operating under perfectly competitive market conditions have no control over price. From the consumers’ perspective, the most desirable market structure is that with the greatest degree of competition, because prices are generally lower. Thus, consumers would prefer as many goods and services as possible to be offered in competitive markets. As often happens in economics, there is a trade-off. While perfect competition gives the largest quantity of a good at the lowest price, other market forms may spur more innovation. Specifically, there may be high costs in researching a new product, and firms will incur such costs only if they expect to earn an attractive return on their research investment. This is the case often made for medical innovations, for example—the cost of clinical trials and experiments to create new medicines would bankrupt perfectly competitive firms but may be acceptable in an oligopoly market structure. Therefore, consumers can benefit from less-than-perfectly-competitive markets. PORTER’S FIVE FORCES AND MARKET STRUCTURE A financial analyst aiming to establish market conditions and consequent profitability of incumbent firms should start with the questions framed by Exhibit 1: How many sellers are there? Is the product differentiated? and so on. Moreover, in the case of monopolies and quasi monopolies, the analyst should evaluate the legislative and regulatory framework: Can the company set prices freely, or are there governmental controls? Finally, the analyst should consider the threat of competition from potential entrants. This analysis is often summarized by students of corporate strategy as “Porter’s five forces,” named after Harvard Business School professor Michael E. Porter. His book, Competitive Strategy, presented a systematic analysis of the practice of market strategy. Porter (2008) identified the five forces as: Threat of entry; Power of suppliers; Power of buyers (customers); Threat of substitutes; and Rivalry among existing competitors. It is easy to note the parallels between four of these five forces and the columns in Exhibit 1. The only “orphan” is the power of suppliers, which is not at the core of the theoretical economic analysis of competition, but which has substantial weight in the practical analysis of competition and profitability. Some stock analysts (e.g., Dorsey 2004) use the term “economic moat” to suggest that there are factors protecting the profitability of a firm that are similar to the moats (ditches full of water) that used to protect some medieval castles. A deep moat means that there is little or no threat of entry by invaders, i.e. competitors. It also means that customers are locked in because of high switching costs. <body><html>

Annotation 1479404031244

Most web browsers display the URL of a web page above the page in an address bar.

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Uniform Resource Locator - Wikipedia
o access an indicated resource, which is not true of every URI. [4] [3] URLs occur most commonly to reference web pages (http), but are also used for file transfer (ftp), email (mailto), database access (JDBC), and many other applications. Most web browsers display the URL of a web page above the page in an address bar. A typical URL could have the form http://www.example.com/index.html , which indicates a protocol ( http ), a hostname ( www.example.com ), and a file name ( index.html ). Conten

Flashcard 1479599328524

Tags

#aviation #wiki

Question

An airplane or aeroplane (informally [...]) is a powered , fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller .

Answer

plane

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An airplane or aeroplane (informally plane) is a powered , fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller .

Original toplevel document

Airplane - Wikipedia
sambiguation) and Aeroplane (disambiguation). [imagelink] [emptylink] North American P-51 Mustang, a World War II fighter [imagelink] [emptylink] The first flight of an airplane, the Wright Flyer on December 17, 1903 An airplane or aeroplane (informally plane) is a powered, fixed-wing aircraft that is propelled forward by thrust from a jet engine or propeller. Airplanes come in a variety of sizes, shapes, and wing configurations. The broad spectrum of uses for airplanes includes recreation, transportation of goods and people, military, and re

Flashcard 1479601949964

Tags

#finance #has-images #steiner-mastering-financial-calculations-3ed

Question

These relationships can be reversed to give:

Answer

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Flashcard 1479604047116

Tags

#deeplearning #neuralnetworks

Question

[...] ( Rumelhart et al. , ; , ). This algorithm has w axed and w aned in p opularity 1986a LeCun 1987 but as of this writing is currently the dominant approach to training deep mo dels.

Answer

back-propagation algorithm

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back-propagation algorithm ( Rumelhart et al. , ; , ). This algorithm has w axed and w aned in p opularity 1986a LeCun 1987 but as of this writing is currently the dominant approach to training deep mo dels.</spa

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Flashcard 1479605619980

Tags

#deeplearning #neuralnetworks

Question

[...] ( Hin ton et al. , 1986 ). This is the idea that each input to a system should b e represen ted b y man y features, and each feature should b e inv olved in the representation of many p ossible inputs.

Answer

distributed represen tation

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distributed represen tation ( Hin ton et al. , 1986 ). This is the idea that each input to a system should b e represen ted b y man y features, and each feature should b e inv olved in the representation of many p

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Flashcard 1479607192844

Tags

#deeplearning #neuralnetworks

Question

distributed represen tation ( Hin ton et al. , 1986 ). This is the idea that [...]

Answer

each input to a system should b e represen ted b y man y features, and each feature should b e inv olved in the representation of many p ossible inputs.

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Flashcard 1479608765708

Tags

#deeplearning #neuralnetworks

Question

Ho c hreiter and Sc hmidh ub er 1997 ( ) in tro duced the [...] net w ork to resolve some of these diﬃculties. T o da y , the [...] is widely used for many sequence mo deling tasks, including many natural language pro cessing tasks at Go ogle

Answer

long short-term memory or LSTM

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Ho c hreiter and Sc hmidh ub er 1997 ( ) in tro duced the long short-term memory or LSTM net w ork to resolve some of these diﬃculties. T o da y , the LSTM is widely used for many sequence mo deling tasks, including many natural language pro cessing tasks at Go ogle<

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Flashcard 1479611125004

Tags

#deeplearning #neuralnetworks

Question

Biological neurons are not esp ecially densely connected. As seen in ﬁgure , 1.10 our mac hine learning mo dels hav e had a num ber of connections p er neuron that w as within an order of magnitude of [...] for decades

Answer

even mammalian brains

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Biological neurons are not esp ecially densely connected. As seen in ﬁgure , 1.10 our mac hine learning mo dels hav e had a num ber of connections p er neuron that w as within an order of magnitude of even mammalian brains for decades

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Flashcard 1479612697868

Tags

#deeplearning #neuralnetworks

Question

In terms of the total n um ber of neurons, neural netw orks hav e b een astonishingly small until quite recently , as shown in ﬁgure . Since the introduction of hidden 1.11 units, artiﬁcial neural net w orks hav e doubled in size roughly every [...] years.

Answer

2.4

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n terms of the total n um ber of neurons, neural netw orks hav e b een astonishingly small until quite recently , as shown in ﬁgure . Since the introduction of hidden 1.11 units, artiﬁcial neural net w orks hav e doubled in size roughly every 2.4 years.<body><html>

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Flashcard 1479614270732

Tags

#deeplearning #neuralnetworks

Question

Unless new technologies allo w faster scaling, artiﬁcial neural netw orks will not hav e the same n um b er of neurons as the human brain un til at least the [...]s

Answer

2050

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Unless new technologies allo w faster scaling, artiﬁcial neural netw orks will not hav e the same n um b er of neurons as the human brain un til at least the 2050s

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Flashcard 1479617416460

Tags

#deeplearning #neuralnetworks

Question

[...] ( Gra v es 2014a et al. , ) that learn to read from memory cells and write arbitrary conten t to memory cells. Suc h neural net w orks can learn simple programs from examples of desired b ehavior. F or example, they can learn to sort lists of num b ers giv en examples of scrambled and sorted sequences. This self-programming technology is in its infancy , but in the future could in principle b e applied to nearly any task

Answer

neural T uring mac hines

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neural T uring mac hines ( Gra v es 2014a et al. , ) that learn to read from memory cells and write arbitrary conten t to memory cells. Suc h neural net w orks can learn simple programs from examples of desired

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Flashcard 1479618989324

Tags

#deeplearning #neuralnetworks

Question

In the con text of [...] an autonomous agen t must learn to p erform a task b y trial and error, without any guidance from the human op erator.

Answer

reinforcemen t learning,

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In the con text of reinforcemen t learning, an autonomous agen t must learn to p erform a task b y trial and error, without any guidance from the human op erator.

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Flashcard 1479622397196

Tags

#matlab #programming

Question

[...] returns the scalar 1 (true) if any element of x is non-zero (true)

Answer

any(x)

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any(x) returns the scalar 1 (true) if any element of x is non-zero (true)

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Flashcard 1479625542924

Tags

#matlab #programming

Question

any(x) returns the scalar 1 (true) if any element of x is [...]

Answer

non-zero (true)

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any(x) returns the scalar 1 (true) if any element of x is non-zero (true)

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Flashcard 1479627115788

Tags

#matlab #programming

Question

[...] returns the scalar 1 if all the elements of x are non-zero

Answer

all(x)

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all(x) returns the scalar 1 if all the elements of x are non-zero

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Flashcard 1479631834380

Tags

#matlab #programming

Question

[...] returns 1 if a is a workspace variable. For other possible return values see help. Note that a must be enclosed in apostrophes

Answer

exist(’a’)

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exist(’a’) returns 1 if a is a workspace variable. For other possible return values see help. Note that a must be enclosed in apostrophes

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Flashcard 1479635766540

Tags

#matlab #programming

Question

[...] returns a vector containing the subscripts of the non-zero (true) ele- ments of x

Answer

find(x)

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find(x) returns a vector containing the subscripts of the non-zero (true) ele- ments of x

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Flashcard 1479637339404

Tags

#matlab #programming

Question

find(x) returns [...]

Answer

a vector containing the subscripts of the non-zero (true) ele- ments of x

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find(x) returns a vector containing the subscripts of the non-zero (true) ele- ments of x

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Flashcard 1479638912268

Tags

#matlab #programming

Question

[...] removes all the zero elements from a!

Answer

a = a( find(a) )

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a = a( find(a) ) removes all the zero elements from a!

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Flashcard 1479640485132

Tags

#matlab #programming

Question

a = a( find(a) ) [what does it do?]

Answer

removes all the zero elements from a!

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a = a( find(a) ) removes all the zero elements from a!

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Flashcard 1479642844428

Tags

#matlab #programming

Question

Another use of find is [...]

Answer

in finding the subscripts of the largest (or smallest) elements in a vector, when there is more than one.

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Another use of find is in finding the subscripts of the largest (or smallest) elements in a vector, when there is more than one.

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Flashcard 1479644417292

Tags

#matlab #programming

Question

[...] returns 1 if x is an empty array and 0 otherwise. An empty array has a size of 0-by-0

Answer

isempty(x)

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isempty(x) returns 1 if x is an empty array and 0 otherwise. An empty array has a size of 0-by-0

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Flashcard 1479645990156

Tags

#matlab #programming

Question

isempty(x) returns 1 [...] and 0 otherwise. An empty array has a size of 0-by-0

Answer

if x is an empty array

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isempty(x) returns 1 if x is an empty array and 0 otherwise. An empty array has a size of 0-by-0

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Flashcard 1479647563020

Tags

#matlab #programming

Question

isempty(x) returns 1 if x is an empty array and 0 otherwise. An empty array has a size of [...]

Answer

0-by-0

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isempty(x) returns 1 if x is an empty array and 0 otherwise. An empty array has a size of 0-by-0

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Flashcard 1479649135884

Tags

#matlab #programming

Question

[...] returns 1s for the elements of x which are +Inf or −Inf, and 0s otherwise

Answer

isinf(x)

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isinf(x) returns 1s for the elements of x which are +Inf or −Inf, and 0s otherwise

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Flashcard 1479652281612

Tags

#matlab #programming

Question

[...] returns 1s where the elements of x are NaN and 0s otherwise. This function may be used to remove NaNs from a set of data.

Answer

isnan(x)

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isnan(x) returns 1s where the elements of x are NaN and 0s otherwise. This function may be used to remove NaNs from a set of data.

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Flashcard 1479653854476

Tags

#matlab #programming

Question

isnan(x) returns 1s where the elements of x are [...] and 0s otherwise. This function may be used to remove NaNs from a set of data.

Answer

NaN

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isnan(x) returns 1s where the elements of x are NaN and 0s otherwise. This function may be used to remove NaNs from a set of data.

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Flashcard 1479655427340

Tags

#biochem #biology #cell

Question

Note that the C–H bond oxi- dation energy in step 6 drives the formation of both [...]. The breakage of the high-energy bond then drives ATP forma- tion

Answer

NADH and a high-energy phosphate bond

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives ATP forma- tion

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Note that the [...] in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives ATP forma- tion

Answer

C–H bond oxi- dation energy

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives ATP forma- tion

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives [...]

Answer

ATP forma- tion

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives ATP forma- tion

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The [...] then drives ATP forma- tion

Answer

breakage of the high-energy bond

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Note that the C–H bond oxi- dation energy in step 6 drives the formation of both NADH and a high-energy phosphate bond. The breakage of the high-energy bond then drives ATP forma- tion

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To compensate for long periods of fasting, animals store fatty acids as fat droplets composed of water-insoluble [...]

Answer

triacylglycerols (also called triglycerides).

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To compensate for long periods of fasting, animals store fatty acids as fat droplets composed of water-insoluble triacylglycerols (also called triglycerides).

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When cells need more ATP than they can generate from the food molecules taken in from the bloodstream, they break down glycogen in a reaction that produces [...], which is rapidly converted to glucose 6-phosphate for gly- colysis

Answer

glucose 1-phosphate

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When cells need more ATP than they can generate from the food molecules taken in from the bloodstream, they break down glycogen in a reaction that produces glucose 1-phosphate, which is rapidly converted to glucose 6-phosphate for gly- colysis

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When cells need more ATP than they can generate from the food molecules taken in from the bloodstream, they break down glycogen in a reaction that produces glucose 1-phosphate, which is rapidly converted to [...] for gly- colysis

Answer

glucose 6-phosphate

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ml>When cells need more ATP than they can generate from the food molecules taken in from the bloodstream, they break down glycogen in a reaction that produces glucose 1-phosphate, which is rapidly converted to glucose 6-phosphate for gly- colysis<html>

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he oxidation of a gram of fat releases about [...] energy as the oxidation of a gram of glycogen

Answer

twice as much

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he oxidation of a gram of fat releases about twice as much energy as the oxidation of a gram of glycogen

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Moreover, glycogen differs from fat in binding a great deal of water, producing a [...] difference in the actual mass of glycogen required to store the same amount of energy as fat.

Answer

sixfold

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Moreover, glycogen differs from fat in binding a great deal of water, producing a sixfold difference in the actual mass of glycogen required to store the same amount of energy as fat.

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If our main fuel reservoir had to be carried as glycogen instead of fat, body weight would increase by an average of about [...]

Answer

60 pounds

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If our main fuel reservoir had to be carried as glycogen instead of fat, body weight would increase by an average of about 60 pounds

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Question

The fats in plants are [...], just like the fats in animals, and differ only in the types of fatty acids that predominate

Answer

triacyl-glyc- erols (triglycerides)

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The fats in plants are triacyl-glyc- erols (triglycerides), just like the fats in animals, and differ only in the types of fatty acids that predominate

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The [...] ( , ) w as an early mo del (1943) of brain function.

Answer

McCulloch-Pitts Neuron

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The McCullo ch-Pitts Neuron ( , ) w as an early mo del McCullo ch and Pitts 1943 of brain function.

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This linear mo del (Mucollach-Piits Neuron model) could recognize tw o diﬀeren t categories of inputs b y testing whether f ( x w , ) is p ositiv e or negative. Of course, for the mo del to corresp ond to the desired deﬁnition of the categories, the w eigh ts needed to b e set correctly . These w eigh ts could b e set by [...]

Answer

the human op erator.

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categories of inputs b y testing whether f ( x w , ) is p ositiv e or negative. Of course, for the mo del to corresp ond to the desired deﬁnition of the categories, the w eigh ts needed to b e set correctly . These w eigh ts could b e set by the human op erator.<body><html>

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This linear mo del [...] could recognize tw o diﬀeren t categories of inputs b y testing whether f ( x w , ) is p ositiv e or negative. Of course, for the mo del to corresp ond to the desired deﬁnition of the categories, the w eigh ts needed to b e set correctly . These w eigh ts could b e set by the human op erator.

Answer

(Mucollach-Piits Neuron model)

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This linear mo del (Mucollach-Piits Neuron model) could recognize tw o diﬀeren t categories of inputs b y [...]. Of course, for the mo del to corresp ond to the desired deﬁnition of the categories, the w eigh ts needed to b e set correctly . These w eigh ts could b e set by the human op erator.

Answer

testing whether f ( x w , ) is p ositiv e or negative

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This linear mo del (Mucollach-Piits Neuron model) could recognize tw o diﬀeren t categories of inputs b y testing whether f ( x w , ) is p ositiv e or negative. Of course, for the mo del to corresp ond to the desired deﬁnition of the categories, the w eigh ts needed to b e set correctly . These w eigh ts could b e set by the human op erator.</

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[...], whic h dates from ab out the same time, simply returned the v alue of f ( x ) itself to predict a real num ber ( Widrow and Hoﬀ 1960 , ), and could also learn to predict these num bers from data.

Answer

The adaptiv e linear elemen t (AD ALINE)

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The adaptiv e linear elemen t (AD ALINE), whic h dates from ab out the same time, simply returned the v alue of f ( x ) itself to predict a real num ber ( Widrow and Hoﬀ 1960 , ), and could also learn to predict these num bers

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The adaptiv e linear elemen t (AD ALINE), whic h dates from ab out the same time, simply returned the [...] ( Widrow and Hoﬀ 1960 , ), and could also learn to predict these num bers from data.

Answer

v alue of f ( x ) itself to predict a real num ber

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The training algorithm used to adapt the weigh ts of the ADALINE w as a sp ecial case of an algorithm called [...] . Sligh tly mo diﬁed versions of the [...] algorithm remain the dominan t training algorithms for deep learning mo dels to da y .

Answer

sto c hastic gradien t descen t

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The training algorithm used to adapt the weigh ts of the ADALINE w as a sp ecial case of an algorithm called sto c hastic gradien t descen t . Sligh tly mo diﬁed versions of the sto c hastic gradien t descent algorithm remain the dominan t training algorithms for deep learning mo dels to da y .

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Mo dels based on the f ( x w , ) used b y the p erceptron and ADALINE are called [...] . These mo dels remain some of the most widely used machine learning mo dels, though in man y cases they are tr aine d in diﬀerent wa ys than the original mo dels were trained

Answer

linear mo dels

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Mo dels based on the f ( x w , ) used b y the p erceptron and ADALINE are called linear mo dels . These mo dels remain some of the most widely used machine learning mo dels, though in man y cases they are tr aine d in diﬀerent wa ys than the original mo dels were trained</s

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Linear mo dels hav e many limitations. Most famously , they cannot [...]

Answer

learn the X OR function,

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Linear mo dels hav e many limitations. Most famously , they cannot learn the X OR function,

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The [...] ( F ukushima 1980 , ) in tro duced a p ow erful mo del architecture for pro cessing images that was inspired b y the structure of the mammalian visual system and later b ecame the basis for the mo dern conv olutional netw ork

Answer

Neo cognitron

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The Neo cognitron ( F ukushima 1980 , ) in tro duced a p ow erful mo del architecture for pro cessing images that was inspired b y the structure of the mammalian visual system and later b ecame the basis

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The Neo cognitron ( F ukushima 1980 , ) in tro duced a p ow erful mo del architecture for pro cessing images that was inspired b y the structure of the mammalian visual system and later b ecame the basis for [...]

Answer

the mo dern conv olutional netw ork

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tml>The Neo cognitron ( F ukushima 1980 , ) in tro duced a p ow erful mo del architecture for pro cessing images that was inspired b y the structure of the mammalian visual system and later b ecame the basis for the mo dern conv olutional netw ork<html>

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Most neural net w orks to da y are based on a model neuron called 9.10 the [...]

Answer

rectiﬁed linear unit .

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Most neural net w orks to da y are based on a model neuron called 9.10 the rectiﬁed linear unit .

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The connectionists b egan to study mo dels of cognition that could actually b e grounded in neural implemen tations ( T ouretzky and Minton 1985 , ), reviving many ideas dating back to the work of psychologist [...] in the 1940s

Answer

Donald Hebb

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l>The connectionists b egan to study mo dels of cognition that could actually b e grounded in neural implemen tations ( T ouretzky and Minton 1985 , ), reviving many ideas dating back to the work of psychologist Donald Hebb in the 1940s<html>

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The [...] b egan to study mo dels of cognition that could actually b e grounded in neural implemen tations ( T ouretzky and Minton 1985 , ), reviving many ideas dating back to the work of psychologist Donald Hebb in the 1940s

Answer

connectionists

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The connectionists b egan to study mo dels of cognition that could actually b e grounded in neural implemen tations ( T ouretzky and Minton 1985 , ), reviving many ideas dating back to the work of psychol

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The central idea in connectionism is [...]

Answer

that a large num ber of simple computational units can ac hiev e in telligen t behavior when net w ork ed together.

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The central idea in connectionism is that a large num ber of simple computational units can ac hiev e in telligen t behavior when net w ork ed together.

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How do you check if two vectors are different?

Answer

This is where any comes in: if any(a ~= b)

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if a ~= b % wrong wrong wrong!!! statement end However this will not work, since statement will only execute if each of the cor- responding elements of a and b differ. This is where any comes in: if any(a ~= b) % right right right!!! statement end

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Edited, memorised or added to reading queue

on 19-Feb-2017 (Sun)

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