Tag trends

Time series week 1

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  • Plotting in R
  • Linear regression properly fitted or not
    • Residue are important thing to observed
    • Q-Q plots for normality test
    • Residues over time
      • Zoomed in residues over time
  • Hypothesis test
    • One, two sided t test
    • Confidence interval
      • Where we think mean lies
      • If it dose not contain 0 we tend to reject null hypothesis (Very broad statement, but I think you got the concept)
  • Correlation function
    • Which quarter data false

 

Ref : https://www.coursera.org/learn/practical-time-series-analysis/home/welcome

 

 

 

Deep learning taking off

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I recently started Andrew Ng’s specialization on deep learning and found these two interesting points :

One is about how performance of algorithm changes with the amount of data. Traditional algorithms have limits but Deep neural network has more advantages.

whyD

 

Also for the small amount of data traditional algorithms may win over neural nets with good feature engineering.

Second reason is that deep learning requires data, computation and efficient algorithms. Recent years have seen significant advancement in algorithm to increase computation efficiency. For example sigmoid to ReLU was an algorithmic change which allowed gradient to converge faster.

 

Ref : https://www.coursera.org/learn/neural-networks-deep-learning/home