In creating code in JavaScript to run calculations on extremely small numbers, MIN_VALUE as validation in the program can be use to check the lowest value JavaScript can handle
Number.MIN_VALUE returns the smallest positive numeric value representable in JavaScript. It is the number more closer to zero. The value is approximately 5e⁻³²⁴.
Using Number.MIN_VALUE, the value can be printed as follows:
val = Number.MIN_VALUE;
console.log(val); // 5e-324
Therefore, In creating code in JavaScript to run calculations on extremely small numbers, MIN_VALUE as validation in the program can be use to check the lowest value JavaScript can handle.
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Answer:
From DRAM to DDR4
Explanation:
RAM stands for <em>Random Access Memory.</em> In 1968, Mr. Robert Dennard at IBM's Watson Research obtained the patent for the one-transistor cell that will eventually substitute the old magnetic core memory allocated in computers of the time. By 1969 Intel released the TTL bipolar 64-bit SRAM (Static Random-Access Memory) as well as the ROM "Read Only Memory"; also in 1969 it evolved into "<em>Phase - change memory - PRAM - </em>". However this evolution was not commercialized, Samsung expressed its interest in developing it. In 1970 the first DRAM product was commercially available; it was developed by Intel. In 1971 it was patented EPROM; in 1978 George Perlegos developed EEPROM.
By 1983 a nice breakthrough happened with the invention of SIMM by Wang Labs. In 1993 Samsung came up with KM48SL2000 synchronous DRAM (SDRAM), this variation soon turned into an inductry standard.
In 1996 DDR began a revolution in the memory sector, then in 1999 RDRAM. Both DDR2 SDRAM. DDRR3 and XDR DRAM were commercialized. Finally in 2007 and 2014 the developments of DDR3 and DDR4 were available for the general public.
Answer:
Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.
Our predictions for the two models were very simmilar. A difference of $32.78 (less than 1% of the total price of the car) is statistically insignificant in this case. Our binned model returned a whole number while the full model returned a more “accurate” price, but ultimately it is a wash. Both models had comparable accuracy, but the full regression seemed to be better trained. If we wanted to use the binned model I would suggest creating smaller bin ranges to prevent underfitting the model. However, when considering the the overall accuracy range and the car sale market both models would be
Explanation:
