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Articles related to "results"


How R and R-Studio can make the writing manuscripts easy for several journals

  • Consider it is a manuscript introducing an R package, the manuscript will be full of R code chunks, R function descriptions, and respective case studies with output plots.
  • It can be really challenging for authors to draft such kind of manuscript with code chunks and other R material in traditional latex format of the journals.
  • This situation can be explained in other words as a researcher is ready with codes, desired results, and plots, but he/she can not use this content directly to his/her manuscript.
  • To avoid such a situation, this post is demonstrating how R and R-Studio can be used to write a manuscript for several journals with minimum efforts and how the R codes chunks and corresponding output can be directly embedded in the manuscript.

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How R and R-Studio can make the writing manuscripts easy for several journals

  • Consider it is a manuscript introducing an R package, the manuscript will be full of R code chunks, R function descriptions, and respective case studies with output plots.
  • It can be really challenging for authors to draft such kind of manuscript with code chunks and other R material in traditional latex format of the journals.
  • This situation can be explained in other words as a researcher is ready with codes, desired results, and plots, but he/she can not use this content directly to his/her manuscript.
  • To avoid such a situation, this post is demonstrating how R and R-Studio can be used to write a manuscript for several journals with minimum efforts and how the R codes chunks and corresponding output can be directly embedded in the manuscript.

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How (NOT) To Predict Stock Prices With LSTMs

  • Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
  • The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
  • Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
  • But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
  • Finally, let’s structure the data so that our LSTM model can easily read them.
  • The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

save | comments | report | share on


How (NOT) To Predict Stock Prices With LSTMs

  • Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
  • The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
  • Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
  • But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
  • Finally, let’s structure the data so that our LSTM model can easily read them.
  • The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

save | comments | report | share on


How (NOT) To Predict Stock Prices With LSTMs

  • Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
  • The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
  • Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
  • But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
  • Finally, let’s structure the data so that our LSTM model can easily read them.
  • The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

save | comments | report | share on


How (NOT) To Predict Stock Prices With LSTMs

  • Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
  • The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
  • Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
  • But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
  • Finally, let’s structure the data so that our LSTM model can easily read them.
  • The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

save | comments | report | share on


Developers leak benchmarks from the Apple silicon Mac transition kit

  • These kits are based on the Mac mini chassis but include ARM-derived Apple silicon rather than Intel CPUs. Before we dig in, it's important to note a few caveats.
  • Accidental or not, the leaks give us some additional information about the potential performance of the new Macs with Apple silicon, though nothing conclusive.
  • Further ReadingApple walks Ars through the iPad Pro’s A12X system on a chipThese tests appear to have been run in Geekbench 5.2.0 for macOS x86 (64-bit)—meaning they were run in Rosetta, Apple's tool for emulating x86 Macs on ARM-based Apple silicon.
  • As for the results, the Apple silicon-equipped developer kits average 811 for single-threaded Geekbench and 2781 for multi-threaded.
  • These tell us what emulation of legacy apps might look like on Apple silicon Macs—and it's likely early adopters of Apple's new ARM-based Macs will use Rosetta to run at least some apps, so it's a potentially useful insight.

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