Apple Machine Learning Journal

  • However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly.
  • An alternative to labelling huge amounts of data is to use synthetic images from a simulator.
  • This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images.
  • We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
  • Read the article View the article “Improving the Realism of Synthetic Images”

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
Continue reading “Apple Machine Learning Journal”

Building a Data Science Portfolio: Machine Learning Project Part 3

Building a #DataScience Portfolio: #MachineLearning Project Part 3

  • We’ll pass balanced to the class_weight keyword argument of the LogisticRegression class, to get the algorithm to weight the foreclosures more to account for the difference in the counts of each class.
  • Define a function to read in the acquisition data.
  • We’ll use cross validation to make predictions.
  • Train a model on groups 1 and 2 , and use the model to make predictions for group 3 .
  • We’ll need to figure out an error metric, as well as how we want to evaluate our data.

Read the full article, click here.


@kdnuggets: “Building a #DataScience Portfolio: #MachineLearning Project Part 3”


The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.


Building a Data Science Portfolio: Machine Learning Project Part 3