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Articles by Mustafa Radif
Total Records ( 2 ) for Mustafa Radif
  Mustafa Radif
  The study presents a new training method for Generative Adversarial Networks (GANs). The new method is about adding low-resolution layers to images in the networks and gradually increasing the resolution until high resolution with good and realistic images are developed. The method helps to create stable images of good quality and the speed of training also, increases by a factor of 2. A score of 8.8 was achieved for unsupervised CIFAR 10. The study presents details of the implementation where rivalry and competition between the two networks are reduced.
  Mustafa Radif and Atheer Alrammahi
  Ad effectiveness on online platforms including Facebook and Google is a challenge for businesses. Since, ad networks use algorithms, ad effectiveness as measured by CTRs is not well understood by marketing and sales executives. CTR prediction with deep neural networks can improve ad CTRs. The AI solution for ad CTRs is useful across industry sectors. In the solution, RNN Models learn representations of sequences by maintaining internal states which are updated sequentially and used as proxy for target prediction. Evidence from research shows that deep neural networks could help businesses enhance CTRs.
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